Scoring of sensed neurological signals for use with a medical device system

ABSTRACT

A medical device system capable of scoring a severity of sensed neurological signals relating to a nervous system disorder. The system comprises a monitoring element that receives a neurological signal having at least one event to be scored. The medical device system identifies one or more features of the neurological signal to use in scoring and computes a score of relative severity of the event using the identified feature. Once two or more events have been scored, the events may be ranked by severity relative to each other.

This application claims priority to U.S. Provisional Application Ser.Nos. 60/418,506 filed Oct. 15, 2002 and 60/503,999 filed Sep. 19, 2003,which are incorporated herein by reference in their entireties.

FIELD OF THE INVENTION

The present invention relates to the detection and the treatment ofnervous system disorders and more particularly to a method and a medicaldevice system for scoring and ranking the relative severity of sensedneurological signals.

BACKGROUND OF THE INVENTION

Nervous system disorders affect millions of people, causing death and adegradation of life. Nervous system disorders include disorders of thecentral nervous system, peripheral nervous system, and mental health andpsychiatric disorders. Such disorders include, for example withoutlimitation, epilepsy, Parkinson's disease, essential tremor, dystonia,and multiple sclerosis (MS). Additionally, nervous system disordersinclude mental health disorders and psychiatric disorders which alsoaffect millions of individuals and include, but are not limited to,anxiety (such as general anxiety disorder, panic disorder, phobias, posttraumatic stress disorder (PTSD), and obsessive compulsive disorder(OCD)), mood disorders (such as major depression, bipolar depression,and dysthymic disorder), sleep disorders (narcolepsy), obesity, andanorexia. As an example, epilepsy is the most prevalent seriousneurological disease across all ages. Epilepsy is a group ofneurological conditions in which a person has or is predisposed torecurrent seizures. A seizure is a clinical manifestation resulting fromexcessive, hypersynchronous, abnormal electrical or neuronal activity inthe brain. (A neurological event is an activity that is indicative of anervous system disorder. A seizure is a type of a neurological event.)This electrical excitability of the brain may be likened to anintermittent electrical overload that manifests with sudden, recurrent,and transient changes of mental function, sensations, perceptions,and/or involuntary body movement. Because the seizures areunpredictable, epilepsy affects a person's employability, psychosociallife, and ability to operate vehicles or power equipment. It is adisorder that occurs in all age groups, socioeconomic classes, cultures,and countries. In developed countries, the age-adjusted incidence ofrecurrent unprovoked seizures ranges from 24/100,000 to 53/100,000person-years and may be even higher in developing countries. Indeveloped countries, age specific incidence is highest during the firstfew months of life and again after age 70. The age-adjusted prevalenceof epilepsy is 5 to 8 per 1,000 (0.5% to 0.8%) in countries wherestatistics are available. In the United States alone, epilepsy andseizures affect 2.3 million Americans, with approximately 181,000 newcases occurring each year. It is estimated that 10% of Americans willexperience a seizure in their lifetimes, and 3% will develop epilepsy byage 75.

There are various approaches in treating nervous system disorders.Treatment therapies can include any number of possible modalities aloneor in combination including, for example, electrical stimulation,magnetic stimulation, drug infusion, and/or brain temperature control.Each of these treatment modalities can be operated using closed-loopfeedback control. Such closed-loop feedback control techniques receivefrom a monitoring element a neurological signal that carries informationabout a symptom or a condition or a nervous system disorder. Such aneurological signal can include, for example, electrical signals (suchas EEG, ECoG, and/or EKG), chemical signals, other biological signals(such as change in quantity of neurotransmitters), temperature signals,pressure signals (such as blood pressure, intracranial pressure orcardiac pressure), respiration signals, heart rate signals, pH-levelsignals, and peripheral nerve signals (cuff electrodes on a peripheralnerve). Monitoring elements can include, for example, recordingelectrodes or various types of sensors.

For example, U.S. Pat. No. 5,995,868 discloses a system for theprediction, rapid detection, warning, prevention, or control of changesin activity states in the brain of a patient. Use of such a closed-loopfeed back system for treatment of a nervous system disorder may providesignificant advantages in that treatment can be delivered before theonset of the symptoms of the nervous system disorder.

During the operation of a medical device system, however, the patient islikely to experience multiple detections of the nervous system disorder.For example, in the case of seizures, the patient may have thousands ofseizures over the course of a time period, but only a few will havebehavioral manifestations. The other seizure episodes that don't exhibitbehavioral manifestations are considered sub-clinical or electrographicseizures. When the medical device system monitors for seizureoccurrences, however, the medical device system will detect many seizureevents although only some of these events will spread to other parts ofthe brain such that the patient will exhibit it (e.g., convulsions,unconsciousness, etc.).

It is therefore desirable to score and/or rank the relative severity ofsensed neurological signals for purposes of detecting, treating and/orreducing the occurrence of the clinical seizures.

BRIEF SUMMARY OF THE INVENTION

In accordance with an embodiment of the invention, the relative severityof sensed neurological signals relating to a nervous system disorder maybe scored and ranked. The medical device system comprises one or moremonitoring elements that generate neurological signals having at leastone event to be scored. The event may be, for example, a detected event,a detection cluster event, and/or a reported event. The event may becharacterized by one or more features. The system thereby identifies oneor more features of the event, computes a score of relative severity ofthe event using the identified feature, and then ranks the event byseverity relative to at least one other scored event. Features for anyevent may include, for example, a maximum ratio, a duration of a seizuredetection, and a spread, number of clusters per unit time, number ofdetections within a cluster, duration of an event cluster, duration of adetection, and an inter-seizure interval. This process of the presentinvention may be performed by computer modules or applications within anexternal device and/or an implanted device.

Once the events have been scored and ranked, they may be communicated toan external device for further processing and/or display.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows one possible embodiment of an external system for treatinga nervous system disorder.

FIG. 2 shows a configuration of a bedside device that is associated withthe external control system of FIG. 1.

FIG. 3 shows a configuration of a sense electronics module that isassociated with the bedside device of FIG. 2.

FIG. 4 shows an embodiment of blanking circuitry that is associated withthe external system of FIG. 1.

FIG. 5 shows a hardware interface that is associated with the bedsidedevice of FIG. 2.

FIG. 6 shows a signal processor that is associated with the bedsidedevice of FIG. 2.

FIG. 7 shows a user interface processor that is associated with thebedside device of FIG. 2.

FIG. 8 is a functional diagram of one embodiment of a seizure detectionalgorithm for use with a medical device system for treatment of aseizure.

FIG. 9 shows one possible embodiment of a hybrid system for treating anervous system disorder.

FIG. 10 is a schematic block diagram of an external component of ahybrid system for treatment of a nervous system disorder.

FIG. 11 is a schematic block diagram of the implantable component of ahybrid system for treatment of a nervous system disorder.

FIG. 12 shows one possible embodiment an implantable system for treatinga nervous system disorder.

FIG. 13 shows an example of a medical device system for infusing drug asthe treatment therapy for treating nervous system disorders.

FIG. 14 is a schematic diagram of a relaying module worn on a patient'swrist for use with a medical device system having implanted components.

FIG. 15 shows a top-level flow diagram for clock synchronization andcalibration for use with an external system.

FIG. 16 shows specific flow diagrams for clock synchronization andcalibration in relation to FIG. 15.

FIG. 17 depicts various graphs of a neurological signal containingflat-lined or clipped artifact data.

FIG. 18 depicts various graphs of a neurological signal containing 60 Hzartifact data.

FIG. 19 shows simulated EEG waveforms, designating an onset of aneurological event.

FIG. 20 shows a flow diagram for a seizure screening procedure to definetreatment therapy according to an embodiment of the invention.

FIG. 21 shows a continuation of the flow diagram that is shown in FIG.20.

FIG. 22 shows data associated with a maximal ratio as determined by aseizure detection algorithm for detecting a cluster.

FIG. 23 shows a timing diagram including the seizure detection algorithmprocessed maximal ratio signal.

FIG. 24 is a flow diagram illustrating the process for implementing acycle mode of operation within generally any medical device system.

FIG. 25 is a flow diagram for phase shifting in accordance with anembodiment of the invention where the nervous system disorder beingtreated is a seizure.

FIG. 26 is a graph of a result of applying the polynomial interpolationphase shift algorithm.

FIG. 27 is a flow diagram for hardware and software blanking.

FIG. 28 depicts an example of the interpolating empirical probabilityfunction for various values.

FIG. 29 depicts an example of the interpolating empirical probabilityfunction for various values.

FIG. 30 depicts an example of the interpolating empirical probabilityfunction for various values.

FIG. 31 depicts an example of the interpolating empirical probabilityfunction for various values.

FIG. 32 depicts an example of the interpolating empirical probabilityfunction for various values.

FIG. 33 depicts an example of the interpolating empirical probabilityfunction for various values.

DETAILED DESCRIPTION OF THE INVENTION

The invention may be embodied in various forms to analyze and treatnervous system disorders, namely disorders of the central nervoussystem, peripheral nervous system, and mental health and psychiatricdisorders. Such disorders include, for example without limitation,epilepsy, Parkinson's disease, essential tremor, dystonia, multiplesclerosis (MS), anxiety (such as general anxiety, panic, phobias, posttraumatic stress disorder (PTSD), and obsessive compulsive disorder(OCD)), mood disorders (such as major depression, bipolar depression,and dysthymic disorder), sleep disorders (narcolepsy), obesity,tinnitus, stroke, traumatic brain injury, Alzheimers, and anorexia.

Moreover, the invention may utilize various treatment therapies fortreating nervous system disorders. Treatment therapies can include anynumber of possibilities alone or in combination including, for example,electrical stimulation, magnetic stimulation, drug infusion, braintemperature control (e.g., cooling), and/or providing a sensory warningto the patient/clinician, as well as any combination thereof.

Each of these treatment modalities may be operated using closed-loopfeedback control or using open-loop therapy. Such closed-loop feedbackcontrol techniques receive one or more neurological signals that carryinformation about a symptom or a condition of a nervous system disorder.Such neurological signals can include, for example, electrical signals(such as EEG, ECoG and/or EKG), chemical signals, biological signals(such as change in quantity of neurotransmitters), temperature signals,pressure signals (such as blood pressure, intracranial pressure orcardiac pressure), respiration signals, heart rate signals, pH-levelsignals, and/or peripheral nerve signals (cuff electrodes on aperipheral nerve). Such neurological signals may be recorded using oneor more monitoring elements such as monitoring electrodes or sensors.For example, U.S. Pat. No. 6,227,203, assigned to Medtronic, Inc.,provides examples of various types of sensors that may be used to detecta symptom or a condition or a nervous system disorder and responsivelygenerate a neurological signal.

Even further, the invention may provide therapeutic treatment to neuraltissue in any number of locations in the body including, for example,the brain (which includes the brain stem), the vagus nerve, the spinalcord, peripheral nerves, etc.

Disclosed herein are three general embodiments of the medical devicesystem—an external system, a hybrid system, and an implantedsystem—however, the invention may be embodied in any number ofconfigurations. The following embodiments may be described with thespecific application of treating epilepsy by electrical stimulation ofthe brain and using closed-loop control monitoring of electricalactivity in the brain. Other embodiments of the invention may useopen-loop therapy, namely treatment therapy that can be providedindependent of information obtained from the monitoring of brainactivity. It will be appreciated, however, that other embodiments of theinvention may treat other nervous system disorders, utilize othertreatment therapies, optionally utilize closed-loop feedback control byreceiving other forms of neurological signals, and/or delivertherapeutic treatment to neural tissue in other locations in the body.Moreover, the medical device system may simply collect data from one ormore of the monitoring elements and provide that data to the patient ortreating physician to further enhance management of the nervous systemdisorder.

EXTERNAL SYSTEM—FIG. 1 shows a system configuration of an externalsystem 100. In an embodiment, the external system 100 is for use in theclinical environment although the external system 100 may also be usedin other environments as well. As disclosed herein, the external system100 provides electrical brain stimulation as the form of treatmenttherapy for purposes of treating seizures or epilepsy as the form ofnervous system disorder. As discussed, however, it will be appreciatedthat system 100 may also be used to provide other treatment therapies atother locations of the body to treat other forms of nervous systemdisorders.

The external system 100 senses electrical brain signals from temporarilyand/or permanently implanted brain electrodes 101, conditions the brainsignals for processing, executes a detection algorithm (e.g., seizurealgorithm 800 in FIG. 8) on the signals to determine the onset,presence, and/or intensity, duration, and spatial extent of neurologicalactivity (e.g., seizure activity), and delivers electrical stimulationin response to selected event detections (e.g., seizure detections). Ofcourse, in other embodiments, the external system 100 may be able todetermine the onset, presence, and/or intensity of other neurologicalevents. The components of the external system 100 may integrate withexisting epilepsy monitoring unit (EMU) preamplifiers 103 and datacollection systems to enable the simultaneous use of customarymonitoring equipment 105.

The external system 100 incorporates a number of programmable parametersand features, some of which are discussed further herein. This affordsthe treating physician and investigators the necessary flexibility toexplore a number of therapeutic paradigms. Data storage, display, andanalysis capabilities are included in the system.

The external system 100 generally comprises a portable bedside device107, a programmer 109, and a number of accessories. Bedside device 107contains hardware and software to perform various functions including,for example, sensing electrical signals recorded by in dwelling brainelectrodes 101, conditioning the signals for further processing by theseizure detection algorithm, executing the seizure detection algorithm,and delivering treatment therapy such as electrical stimulation. Thoseskilled in the art will appreciate, however, that these functions of thebedside device 107 may be performed in other components of the externalsystem 100.

Electrodes 101 are typically placed in the brain or on the surface ofthe brain or in the bone of the skull. Electrodes 101 could be placedbetween the surface of the skull and the scalp, within the scalp, overthe scalp, or outside the body on the skin surface. Electrodes 101 arecoupled to the bedside device 107 through input jacks compatible withstandard electrode extensions and connectors. Electrode output jacks onthe box provide a means for passing raw brain signals to existing EMUequipment. A serial port supports the real-time transfer of data betweenthe programmer 109 and the bedside device 107. As discussed herein,electrodes 101 may take any number of forms including, but not limitedto, temporary subdural grid and strip electrodes, temporary depthelectrodes, deep brain stimulation (DBS) electrode systems, and/or acombination of several different electrode types. Although in anembodiment, the external system 100 utilizes eight electrodes, it willbe appreciated that greater or fewer electrodes may be utilized.Moreover, other forms of communication may also be utilized between thevarious components including wireless, infrared, radio frequency (RF),and/or computer network (e.g., LAN, WAN, Internet).

The external system 100 utilizes a programmer 109, which in theembodiment is a commercially available personal computer and anoperating system configured with custom external system applicationsoftware. Those skilled in the art will appreciate that anygeneral-purpose computing device may be used including, but not limitedto, a hand-held device. Other communication techniques, of course, mayalso be utilized including a telemetry device. The programmer 109 maydisplay in real-time the brain signals being processed by the system,the corresponding detection criteria for automated seizure detection,and other pertinent system and session information. All programmablesystem parameters, including electrode designation, algorithmparameters, and stimulation output parameters, may be adjusted throughthe programmer 109. Investigators may also use the programmer 109 toperform secondary functions, such as off-line algorithm analysis andadaptation.

Accessories for the external system 100 include a serial cable 111 forconnecting bedside device 107 to programmer 109, a medical grade 6 Vdcpower supply for primary power to bedside device 107, a supply ofbatteries for bedside device 107, and an event marker junction box (notshown). The event marker junction box allows the patient, or anyoneelse, to manually record the onset of an event of significance, such asa seizure (an ictal event). Data may be collected during the event andsent simultaneously to the bedside device 107 and the EMU equipment. Theevent marker junction box allows the patient event marker signal to beinput to both the EMU equipment and the bedside device 107simultaneously. In the embodiment, bedside device 107 is coupled to theevent marker junction box and to an event input of the EMU equipment.The ability to simultaneously input the event marker in the EMUequipment and the bedside device 107 also serves to synchronize eventsrecorded/stored in both the EMU equipment and the bedside device 107.With a variation of the embodiment, a time drift may be determined. Thetime drift is indicative of a time difference of the bedside device 107with respect to the EMU equipment.

Temporary diagnostic electrodes, manufactured by Adtech, Radionics, andPMT Corp. among others, may be used for recording brain signals to aidin the identification of the areas responsible for seizure generation.Electrodes 101 are typically placed intracranially, on the surface ofthe brain (subdural grids and strips) or within brain tissue (depthelectrodes), near areas suspected of being epileptogenic. ECoG signalsfrom these electrodes 101 are recorded on external EEG monitoringequipment (Grass/Telefactor, Nicolet Biomedical, Bio-logic, and others)and evaluated by the physician to determine the zone(s) ofepileptogenesis.

In addition to monitoring, electrodes 101 may conduct stimulus pulsesgenerated by a stimulator to map the functional areas of the brainunderlying electrode 101. In this manner, the physician is able todetermine the risks and benefits associated with a possible surgicalapproach to treat the patient's epilepsy.

The external system 100 may also support deep brain stimulation as atreatment for intractable epilepsy. DBS leads may be placed withinstructures of the brain (e.g., thalamic nuclei, subthalamus, temporallobe, etc.) to enable the continuous or intermittent delivery ofelectrical stimulation to structures that may have a network or localeffect on areas of epileptogenesis. ECoG recordings may also be obtainedfrom the DBS leads.

When the system 100 is set up with the EMU, the externalized ends of theimplanted electrodes 101 will connect directly to the bedside device107. The raw signals collected by the electrodes 101 connected tobedside device 107 are processed by the external system 100 and passedto existing EMU preamplifier 103 and into EMU data collection system105. Additional electrode connections may occur directly between thepatient and existing EMU preamplifier 103 and data collection system105, bypassing the external system 100, to enable recording from agreater number of electrode contacts than used by bedside device 107. Bymeans of a serial cable 111, the bedside device 107 interfaces with theprogrammer 109 through which system programming, data display, andreal-time and/or retrospective analysis may take place.

FIG. 2 is a schematic block diagram depicting the bedside device 107,which is a component of the external system 100. The bedside device 107comprises a sense electronics module 201 for processing (i.e.,amplifying and digitizing) the sensed neurological signal, a stimulationelectronics module 203 for providing treatment therapy, a hardwareinterface processor 205 for controlling the sense and stimulationelectronics modules and passing the digitized EEG data to a signalprocessor 207 (which performs detection algorithm and control systemtiming and operation), a user interface processor 209 for controllingserial data to and from the signal processor 207 and the programmer 109,and a power supply 211.

FIG. 3 is a schematic block diagram depicting the sense electronicsmodule 201 associated with the bedside device 107. Sense electronicsmodule 201 processes EEG signals from the electrodes 101 so that the EEGsignals can be further processed by the signal processor 207. A blankingcircuitry 301 comprises optically coupled relays. Blanking circuitry 301provides independent blanking of any channel electronics (e.g., anamplifier) that is associated with an electrode. Blanking circuitry 301disconnects the channel received from the electrode when the electrodeis being stimulated. A differential amplifier 303 provides buffering andisolation from electronics associated with other channels by having ahigh common mode rejection. A notch filter 305 removes residual 50 or 60Hz noise signal component that may be attributable to powering theexternal system 100 from alternating current (AC). A sampling circuitry307 converts an analog signal associated with each channel into adigital signal with an analog to digital converter. In the embodiment,sampling circuitry 307 provides eight bit resolution with a 250 Hzsampling rate, an adjustable gain, and adjustable analog filter corners.Those skilled in the art will appreciate that the digital precision andsampling rates may be increased or decreased according to the particularapplication or sensed signal.

FIG. 4 is a schematic block diagram depicting an embodiment of blankingcircuitry associated with the external control system 100. An opticallycoupled relay 405 is associated with an input 401 and an output 403.Blanking circuitry 301 controls relay 405 through control signal 407 sothat output 403 is isolated from input 401 when the associated electrodeis being stimulated. The circuit also ensures that the amplifier inputduring this time is not floating to prevent drifts in the voltagesrecorded by the system.

FIG. 5 is a schematic block diagram depicting the hardware interfaceprocessor 205 associated with the bedside device 107. Hardware interfaceprocessor 205 comprises a micro controller 503 that communicates via atri-state bus interface 501 to control blanking circuitry 301, senseelectronics module 201, and stimulation electronics module 203. It alsonotifies signal processor 207 when data is available for furtherprocessing.

FIG. 6 is a schematic block diagram depicting the signal processor 207associated with the bedside device 107. Signal processor 207 comprises aprocessing engine 601 (e.g., Analog Devices ADSP2189M), an SRAM andflash memory 605, which is used for loop recording, a real time clock603, which is used for associating a time with loop recording, and aboot flash memory 607, which loads a program on powering up signalprocessor 207.

FIG. 7 is a schematic block diagram depicting the user interfaceprocessor (UIP) 209 associated with the bedside device 107. A processor701 receives data and commands through user buttons 703. Processor 701may be a single device or may consist of multiple computing elementssuch as a Digital Signal Processor (DSP). The UIP 209 may provide aRS-232 interface 709 between the programmer 109 and the processor 701.Moreover, a component of the UIP 209 and processor 701 may communicatewith each other to convey other information (such as button press datafrom user buttons 703 to the processor 701, and icon status data fromthe processor 701 to an LCD display 705). Processors (e.g., DSPs)associated with processor 701 may also utilize analog circuitry.Additionally, RS-232 interface 709 enables information to be sent toprocessor 701 from the user. Processor 701 responds to the data andcommands from the user by displaying and updating a menu display on theLCD display 705. The patient may input a marker, signifying an eventsuch as a seizure, through isolated patient marker 707. An alarm 711 mayalert the user or the patient about an event such as a detected orpredicted seizure.

As discussed, the external system may be implemented with the specificapplication of treating epilepsy by electrical stimulation of the brainusing, as one of the possible options, closed-loop control based onmonitoring of electrical activity in the brain. In such an embodiment, aseizure detection algorithm may be utilized to monitor the electricalbrain activity and predict whether a seizure onset is about to occur ordetect its onset. In accordance with an embodiment, the seizuredetection algorithm is that disclosed in U.S. Pat. No. 5,995,868(entitled “System for the Prediction, Warning, Prevention, or Control ofChanges in Activity States in the Brain of a Subject”). Otherembodiments may utilize variations of the seizure detection algorithm ormay use other detection algorithms for detecting seizures and/or othernervous system disorders. Moreover, the detection algorithm may beadaptable. Discussed below is an overview of a preferred embodiment ofthe seizure detection algorithm followed by an example of how thealgorithm may be adaptable.

FIG. 8 shows a functional diagram of an example of a seizure detectionalgorithm 800 that may be used. Generally, the seizure detectionalgorithm 800 is capable of detecting brain activity changes based onthe spectral characteristics, intensity (ratio), spread, and duration ofan electrical (EEG, ECoG, and/or EKG) signal 801 that is obtained from aset of electrodes. In the embodiment of external system 100, eight ECoGchannels may be supported, although other embodiments may support adifferent number of channels. The analog EEG or ECoG data from theelectrodes 101 are transformed to digital data with an A to D converterin the bedside device 107. In the hybrid system 1000, the A to Dconverter may be in the implantable device 953. A digital filter such asa finite impulse response (FIR) filter 803 is configured to estimate thepower spectrum density characteristics of a set of electrical brainsignals. A foreground determinator 805 associates a foreground value ofthe signals with a moving foreground interval of a predetermined timelength (e.g., 2-seconds), which may be programmable. In the embodiment,foreground determinator 805 squares the value of each sample in theforeground interval and selects the median value. A backgrounddeterminator 807 associates a background value with a moving backgroundinterval of predetermined time length (e.g., 30 minutes), which againmay be programmable. At any point in time, the current foreground andbackground values are computed, respectively, from the foreground andbackground intervals that immediately precede that time point.Background determinator 807 squares the value of each sample in thebackground interval and selects the median value. The seizure detectionalgorithm 800 then processes the results of background determinator 807through an “exponential forgetting” adjustor 809 that combines theresults with previous results from background determinator 807 toproduce an exponentially-smoothed background value. A module 811 thendivides the foreground value by the exponentially-smoothed backgroundvalue to determine a ratio for each signal from each electrode in aselected electrode group. Module 811 also determines the largest ratiofrom the group of electrodes. The value of the largest ratio is then fedinto a detection criterion module 813, which analyzes the sequence oflargest ratios to determine when an event is detected. Output 814 fromalgorithm 800 includes notification that an event has occurred(“detection”) as well as variables quantifying the event (e.g., ratio,extent of spread, and duration from all electrodes).

As discussed, the external system 100 may take other forms including,for example, a hybrid control system and an implantable control system.The functionalities described herein may be performed by any of theseembodiments, in which some of the functionalities may be associated withdifferent components of the various embodiments.

HYBRID SYSTEM—FIG. 9 shows an embodiment of a hybrid system 1000 fortreatment of a nervous system disorder in accordance with one embodimentof the invention. As discussed, although the hybrid system 1000 isdiscussed in the context of providing brain stimulation for treatingepilepsy, it will be appreciated that the hybrid system 1000 may also beused to provide other treatment therapies at other locations of the bodyto treat other forms of nervous system disorders.

Referring still to FIG. 9, leads 951 are coupled at a distal end toelectrodes that sense brain activity of the patient and deliverelectrical stimulation to the patient. At a proximal end, leads 951 arecoupled to extension wire system 952 that in turn connects to animplantable device 953. The connection between leads 951 and extensionwire system 952 typically occurs under the scalp on top of the craniumat a convenient location such as behind and above the ear. The distalconnector of extension wire system 952 is designed to accommodate thevarious options for leads 951 which might be selected by the surgeon torecord and/or stimulate from deep within the brain, on the surface ofthe cortex or from electrodes just protruding through the skull or onthe surface of the skull. Extension wire system 952 is passed just underthe skin along the lateral aspect of the neck to connect withimplantable device 953. Leads 951 typically last at least as long asextension wire 952. Extension wire 952 is made of materials that allowit to withstand considerable stress/forces caused by neck movement. Theimplantable device 953 conditions signals, samples the signals, anddelivers electrical stimulation through the electrodes 951. An antenna955 supports telemetric communications between the implantable device953 and an external device 950. The external device 950, which may be anexternal wearable digital signal processing unit, receives sampledsignals from the implantable device 953, detects seizures, and sendssignals to the implantable device 953 to initiate stimulation therapy.

FIG. 10 is a schematic block diagram of the external device 950 for thehybrid control system of FIG. 9. The external device 950 communicates(continuously or intermittently) with the implantable device 953 over atelemetry link 1001 through an uplink/downlink circuit 1003 or through acabling arrangement. The external device 950 may interface with aprogrammer 1021 (such as programmer 209) through RS232 interface 1017.The programmer 1021 may be a physician programmer, a patient programmer,or any general-purpose computing device having software for interfacingwith a medical device system.

An apparatus 1000 (e.g., the external device 950) is powered by arechargeable/replaceable battery 1025 and is voltage regulated by avoltage regulation circuit 1019. A DSP controller 1005 processesneurological data from implantable device 953 and records/storesprocessed data in a boot flash memory 1007 or in a compact flash memory1023, which extends the recording capability of memory 1007. Theapparatus 1000 may be instructed by a user through buttons 1013. Thecorresponding inputted information is received by a peripheral interfacecontrol (PIC) microprocessor 1011 through a RS232 interface 1017. Theuser may instruct the DSP controller 1005 to process, store, andretrieve neurological data through PIC microprocessor 1011. The DSPcontroller 1005 is coupled to a memory 1009 and a speaker 1027. Also,the user may obtain information (e.g., status and selected processeddata) through an LCD screen 1015.

FIG. 11 is a schematic block diagram of the implantable device 953 forthe hybrid control system of FIG. 9. An apparatus 1100 (e.g., theimplantable device 953) is implanted in conjunction with a set ofelectrodes 1101. (In the exemplary embodiment shown in FIG. 11, the setof electrodes 1101 comprises eight electrodes.) A reference electrode1103 is another electrode that is not included in the set of electrodes1101 and that is not typically involved with the neurological activityas the set of electrodes 1101. The apparatus 1100 communicates with theexternal device 1000 through a telemetry transceiver 1127 that iscoupled to control registers 1109, an antenna 1125, and a telemetry link1123. The apparatus 1000 (e.g., the external device 950) may collectdata from the apparatus 1100 by placing a patch antenna 955 on thepatient's body over the implantable device 953 to thereby communicatewith antenna 1125 of the apparatus 1100.

Each electrode of the set of electrodes 1101 may either receive aneurological signal or may stimulate surrounding tissue. Stimulation ofany of the electrodes contained in the electrode set 1101 is generatedby a stimulation IC 1105, as instructed by a microprocessor 1119. Whenstimulation is generated through an electrode, the electrode is blankedby a blanking circuit 1107 so that a neurological signal is not receivedby channel electronics (e.g., amplifier 1111). When microcontroller1119, which is coupled to 1 MHz crystal oscillator 1121, determines thata channel shall be able to receive a neurological signal, an analog todigital converter (ADC) 1113 samples the neurological signal at adesired rate (e.g., 250 times per second). The digitized neurologicalsignal may be stored in a waveform memory 1115, which is coupled to themicrocontroller 1119 via AD bus 1117, so that the neurological data maybe retrieved by the apparatus 1000 when instructed.

IMPLANTED SYSTEM—FIG. 12 shows an embodiment of an implanted system 10for treatment of a nervous system disorder in accordance with anotherembodiment of the invention. As discussed, although the implanted system10 is discussed in the context of providing brain stimulation, it willbe appreciated that the implanted system 10 may also be used to provideother treatment therapies at the brain or head or at other locations ofthe body. The implanted system 10 generally includes an implanted device20 coupled to one or more therapy delivery elements 30. The therapydelivery elements 30, of course, may also serve as monitoring elementsto receive a neurological signal. The implanted device 20 maycontinuously or intermittently communicate with an external programmer23 (e.g., patient or physician programmer) via telemetry using, forexample, radio-frequency signals. In this embodiment, each of thefeatures and functionalities discussed herein are provided by theimplanted device 20. As depicted, the external programmer 23 is coupledto a coil antenna 24 via wire 24 a.

Those skilled in the art will appreciate that the medical device systemsdescribed above may take any number of forms from being fully implantedto being mostly external and can provide treatment therapy to neuraltissue in any number of locations in the body. For example, the medicaldevice systems described herein may be utilized to provide vagal nervestimulation, for example, as disclosed in U.S. Pat. No. 6,341,236(Osorio, et al.). In addition, the treatment therapy being provided bythe medical device systems may vary and can include, for example,electrical stimulation, magnetic stimulation, drug infusion (discussedbelow), and/or brain temperature control (e.g., cooling). Moreover, itwill be appreciated that the medical device systems may be utilized toanalyze and treat any number of nervous system disorders. For example,various U.S. Patents assigned to Medtronic provide example of nervoussystem disorders that can be treated. In the event that closed-loopfeedback control is provided, the medical device system can beconfigured to receive any number of neurological signals that carryinformation about a symptom or a condition or a nervous system disorder.Such signals may be provided using one or more monitoring elements suchas monitoring electrodes or sensors. For example, U.S. Pat. No.6,227,203, assigned to Medtronic, Inc., provides examples of varioustypes of sensors that may be used to detect a symptom or a condition ora nervous system disorder and responsively generate a neurologicalsignal and is incorporated herein in its entirety.

As an example to illustrate other embodiments of treatment therapies,FIG. 13 shows a medical device system 110 that may be implanted below askin 125 of a patient for delivery of drug to a patient as the form oftreatment therapy. Device 10 has a port 14 into which a needle can beinserted through the skin to inject a quantity of a liquid agent, suchas a medication or drug. The liquid agent is delivered from device 10through a catheter port 20 into a catheter 22. Catheter 22 is positionedto deliver the agent to specific infusion sites in a brain (B)throughbone 123 via distal end 115 of tube 22 a, although any location in thebody may be utilized. As depicted, sensor 130 may include individualelectrodes 26, 28 and 30 positioned on a tube. As it relates to thedelivery of drug, device 10 may take a form of the like-numbered deviceshown in U.S. Pat. No. 4,692,147 (Duggan), assigned to Medtronic, Inc.,Minneapolis, Minnesota and is incorporated herein in its entirety. Thedevice 10 may be augmented to provide the various functionalities of thepresent invention described herein.

Discussed herein are various features and functionalities that one ormore of the above-described medical device systems may incorporate.Where applicable and although not required, these features andfunctionalities will be described in the general context ofcomputer-executable instructions, such as program modules. Generally,program modules include routines, programs, objects, scripts,components, data structures, and the like that perform particular tasksor implement particular abstract data types. Moreover, these featuresand functionalities may reside in any number of locations within themedical device system including either one of the implanted componentsand/or one of the external components.

Relaying Module for Treatment of Nervous System Disorders

In the hybrid system 1000 and the implanted system 10 embodiment,greater telemetric portability may be achieved between the implantedcomponent and the external component by providing a relaying module.

FIG. 14 discloses one embodiment of such a relaying module in the formof a device that is worn, for example, on the patient's wrist. In suchan arrangement, the implanted component 1405 of the medical devicesystem communicates with the relaying module 1415 via telemetry antenna1410. Similarly, the external component, which includes an externalwearable signal processor 1425 that is coupled to audio output 1430 andis in communication with physician programmer 1435, communicates withthe relaying module 1415 via antenna 1420. In the embodiment, atelemetry link 1421 between relaying module 1415 and antenna 1420comprises a 3 MHz body wave telemetry link. To avoid interference, therelaying module 1415 may communicate with the external and implantedcomponents using differing communication schemes. In some embodiments,the reverse direction and the forward direction of telemetry link 1421may be associated with different frequency spectra. The relaying module1415 thereby provides a greater range of communications betweencomponents of medical device system. For example, in the embodiment ofthe implanted system 10, the external programmer 23 may communicate withthe implanted device 20 from a more remote location. The externalprogrammer 23 may be across the room and still be in communication viathe relaying module 1415. Similarly, in the embodiment of the hybridsystem 1000, the external device 950 may be located further away thanbeing worn by the patient. With the telemetry booster stage, the use ofhybrid system 1000 is more convenient to the patient in particular atnight while sleeping or when taking a shower, eliminating the need forthe external device 950 to be worn on the body.

Synchronized and/or Calibrated Clocks Providing Treatment Therapy to aPatient

Obtaining, processing, storing and using information that evolves overtime and using multiple devices requires adequate time synchronizationbetween different clocks used by the respective devices for meaningfulinterpretation and use of this information for control or otherpurposes. With a medical device system, it may be important thatdifferent clocks, in which at least one of the clocks is associated withthe medical device system, be synchronized. For example, propersynchronization is required between an epilepsy monitoring system clockused to timestamp the onset time of a clinical seizure and that of amedical device used to record, analyze and timestamp the subject's brainwaves. Knowing whether particular EEG signal changes precede or followbehavioral changes (and by how long) is important in assessing whetherthe electrodes from which the signal was obtained is at or near theseizure focus. The importance of clock synchronization is furtherexemplified in the discussion of screening below.

Any one of the above-described medical device systems may be configuredto provide synchronization and calibration of all system clocks.(Moreover, the embodiment may support configurations in which one ormore of the clocks are associated with an entity that is different froma medical device system.) Where an embodiment of the medical devicesystem comprises more than one system clock, it may become desirable toensure that the clocks are synchronized with each other. For example, inthe embodiment of the external system 100, the system comprisesmonitoring equipment 105, bedside device 107, and programmer 109, inwhich each component may have separate clocks. In order to coordinatethe clocks, bedside device 107 provides a synchronization andcalibration process that enables the plurality of clocks to be alignedwithin a desired accuracy. It will be appreciated, however, that thesynchronization process may be implemented within any other component.

FIG. 15 shows a top-level flow diagram for a clock synchronization andcalibration process 1500. For clarity, the following discussion isprovided in the context of the external system 100, although otherembodiments are possible. The process starts at step 1501 and in step1503, a user initiates a study and sets-up the parameters throughprogrammer 109 in step 1505. In the embodiment, the user enters aselected time (through programmer 109) that is different (which may begreater) than the reference time that is associated with monitoringequipment 105. (The reference time may comprise the associated date suchmonth and day.) When the user determines that the time associated withmonitoring equipment 105 equals the selected time, the user synchronizesthe clocks in step 1507. Consequently, programmer 109 may generate acontrol message to bedside device 107 to synchronize the clock ofbedside device 107. In the embodiment, the user selects an icon;however, other embodiments may use a Global Positioning System (GPS)clock reference or use a control line from monitoring equipment toactivate the synchronization of clocks. In step 1509, programmer 109determines if the clocks of bedside device 107 and programmer 109 weresuccessfully synchronized and notifies the user through a real-time datadisplay of programmer 109. In step 1511, the external system 100 startsrun mode operation in which the medical device system may operate itsintended functions.

During the operation of the external system 100 over time, the clocks ofmonitoring equipment 105, programmer 109, and bedside device 107 maydrift with respect to each other. In the embodiment, the clocks ofprogrammer 109 and bedside device 107 are calibrated using the clock ofmonitoring equipment 105 as a reference. In step 1513, the programmer109 notifies the user that calibration should be performed (e.g., every12 hours, although other time periods may be utilized). The userconsequently enters a selected time (through programmer 109) that isgreater than the present time that is associated with monitoringequipment 105. When the user determines that the time associated withmonitoring equipment 105 equals the selected time, the user calibratesthe clocks in step 1513. With the calibration process, the clocks ofbedside device 107 and programmer 109 are not modified. Rather a “drift”time (equal to the difference between the clock in bedside device 107and monitoring equipment 105) is stored to a file. Data that aresubsequently collected by bedside device 107 can be correlated to thetime of monitoring equipment 105 by adjusting the time of bedside device107 by the drift time. (In the embodiment, the drift time is determinedby the difference between the current time of the second clock and thereference time of the first clock.) However, if the drift time isdetermined to be greater than a predetermined threshold (e.g., onesecond) in step 1515, programmer 109 may notify the user that the clocksneed to be re-synchronized or more frequently calibrated to accuratelytrack the drift between the clocks. If that is the case, the clocks aresynchronized in step 1517. In step 1519, the operation is continued.

FIG. 16 shows specific flow diagrams for clock synchronization andcalibration in relation to FIG. 15. Steps 1601, 1603, 1605, 1607 and1609 correspond to synchronizing the clocks in the external system 100as shown in steps 1507 and 1517. Steps 1611, 1613, 1615, 1617 and 1619correspond to manually calibrating the clocks as shown in step 1513.Additionally, as shown in steps 1621, 1623, 1625, 1627 and 1629, theexternal system 100 may periodically (e.g., every 10 minutes) calibratethe clocks of the programmer 109 and bedside device 107 withoutrequiring intervention by the user. In step 1623, programmer 109retrieves the time from bedside device 107. Programmer 109 compares itstime with the retrieved time from bedside device 107 and calculates anupdated drift time. Programmer 109 stores the adjusted drift time forcorrelating times subsequently. As discussed, synchronization may alsobe utilized in either the hybrid or implanted systems. For example, inthe embodiment of the implanted system, the implanted device may provideto or receive from an external component (e.g., patient or physicianprogrammer, video equipment, testing equipment) a clocksynchronization/calibration signal, and the calibration/synchronizationtechniques discussed herein may be utilized to correspond the implanteddevice with the one or more external devices. Moreover, the clockreference (i.e., the reference clock to which all other clocks would besynchronized/calibrated) may be the clock in the implanted component,one of the external components, a GPS clock, an atomic clock, or anyother reference clock.

Other embodiments of the invention may determine a delay time betweeninitiating the synchronization of clocks and the time that a clockreceiving an indication to synchronization of the clock. For example, asecond clock may receive a command of a radio channel from a first clockthat provides a reference time. The propagation time for receiving aradio signal is approximately 5 microseconds for each additional mile indistance between the first clock and the second clock.

With other embodiments of the invention, synchronization of two clocksmay occur on a periodic basis, e.g., every 24 hours. Also, as withembodiments using an atomic clock or a GPS clock, time adjustments thatare associated with time zones may be supported. For example, the firstclock and the second clock may be located in different time zones. Insuch a case, the second clock may be synchronized in accordance with thetime zone that the second clock is situated. As another example, thesecond clock may be synchronized to adjust the time in accordance with atransition between standard time and daylight savings time.

Signal Quality Monitoring and Control

In accordance with another feature of the present invention, any one ofthe above-described medical device systems may be programmed so that itmay monitor received neurological signals and analyze them to determinetheir quality. If a parameter of the received neurological signal for aparticular monitoring element falls outside a certain range indicativeof a degraded signal, the medical device system may recognize the signalas having poor quality until such time as the signal is restored tosufficiently good quality. Upon making the determination of poorquality, for example, the medical device system may decide to remove thesignal from consideration in providing closed-loop feedback control.Alternatively, such signal quality monitoring may be utilized for dataanalysis in which case the degraded signal would be removed fromconsideration in the processing for data analysis. The sensed signalsand quantitative analysis of their quality may also be provided to atreating physician or the medical device manufacturer. In addition, suchentities and/or the patient may be notified of the existence of adegraded signal and/or that the signal has been removed from processing.This feature thereby removes from processing any neurological signalduring periods when the signal quality is “sufficiently poor” and toresume processing of the signal once it is restored to “sufficientlygood quality.” For example, in the above-described application of aseizure detection system, a signal received from an electrode is removedfrom being provided as an input to the seizure detection algorithmduring the time period that it is determined that the signal is of poorquality. For example, a signal having “clipping” (i.e., flatline) datamay be indicative the signal exceeding voltage limits of the medicaldevice system or experiencing amplifier saturation clipping.

The medical device system may calculate one or more variables thatquantify the quality of the neurological signal received from each ofthe monitoring elements. For each variable data points of the receivedneurological signal may be gathered and analyzed within a given movingwindow. The percentage of data points with associated signal qualityvariable falling above or below a predetermined range may be determinedand monitored as the window moves with time. The resulting percentagevalues will be numbers between 0 and 100, representing quality, so thatthe medical system can quantify the quality of each associated window ofdata points. The medical system may use the computed quality to acceptor reject data during monitoring. Depending on the embodiment of themedical device system, this process may be implemented as softwaremodules within any one of the components of the external system, eitherthe implantable device 953 or the external device 950, or within theimplantable system 10. Each quality variable of the received rawneurological signal, or processed signals obtained through sometransformation of the neurological signals to be used in subsequentprocessing, may be continuously and independently monitored. Separatesoftware modules may exist for each quality variable of the signalsbeing monitored.

As an example, the medical device system may monitor excessiveflat-lining, which is distinguishable from that which may occur pre- orpost-ictally, for a particular neurological signal. For example, adegraded signal may be one in which 40% of the signal values are clipped(e.g., 40% of the data points within the moving window are at the upper“rail”). Such a signal may be considered of poor quality and henceundesirable, as compared to one that has no clipping or only minimalclipping (e.g., 4-5%). The medical device system may therefore takeinstantaneous signal quality measurements and performexponential-smoothing or some other averaging over a moving window. Thelength of the moving window may vary for different embodiments or overtime and can have a 60-second duration in one embodiment. The medicaldevice system may thereby compare the level of change between twoadjacent data points in the moving window. If the data points' “qualityvariables” are within a predetermined value of each other, it isindicative of flat-lining. For example, if two adjacent data points areless than 2 bits of signal digital precision from (or, alternatively,within 10% of) each other, it may be determined to be indicative ofinstantaneous flat-lining. Another indication of flat-lining is wheresignals from two adjacent monitoring elements are identical. The medicaldevice system may therefore ignore or substitute signals from a specificelectrode if the number of flat-line data points in a given rollingwindow exceeds a predetermined amount or percentage relative to thetotal number of data points in the time window. Once the number offlat-line data points falls below a second (typically lower)predetermined amount, the medical device system may then re-enable thesignal from that electrode to be used for processing (e.g., dataanalysis or closed-loop feedback control). The system may also providenotification to the physician and/or the patient of these events such asa sensory signal (e.g., alarm). Alternatively, the signal experiencingpoor signal quality removed from processing may be replaced with asubstituted signal. The substituted signal may be, for example, a signalthat provides typical signal characteristics or may provide signalcharacteristics received from a neighboring monitoring element.

More particularly, a neurological signal is instantaneously flat-linedif the two-point signal differential is sufficiently small, i.e., if:|x _(K+1) −x _(k) |≦C ₁where C₁ is a specified parameter of the method. For example, C₁=0causes clipping to be defined in the case of a strictly zero signaldifferential. Setting C₁ slightly larger than zero (e.g., correspondingto a few bits of precision in the input signal) allows potentiallyundesirable flat-lining that isn't exactly zero in differential to alsobe detected.

Even if the signal is of good quality, there may still be pairs of datapoints that will satisfy the above definition of being instantaneouslyflat-lined. This can occur, for example, around local extrema of thesignal (when the first derivative is supposed to be zero). To allow thesystem to ignore such instances, a time-average of the indicatorfunction of instantaneous flat-lining is provided. To minimize memoryrequirements and computational effort, the time-average may beimplemented using exponential forgetting. In this manner, the systemgenerates a flat-line-fraction signal, which may be interpreted as thefraction of time that the signal was flat-lined in the most recent timewindow. The flat-line-fraction signal is initialized to zero, and has avalue in the interval [0,1] at all times. The timescale of the movingwindow (i.e., its effective length) is determined by an exponentialforgetting parameter, λflat, and may be quantified by the correspondinghalf-life of the exponential forgetting. The signal sampling rate, Fs,describes the relationship between λflat and the half-life, T1/2 as:0.5=λ_(flat) ^(T) ^(1/2) ^(F) ^(s)

The parameters λ_(flat) and C₁ should be chosen to ensure that thewindow of monitoring for the flat-line detection is sufficiently long toavoid disabling analysis for seizure detection just prior to anelectrographic seizure that is preceded by signal quieting that may be atypical seizure onset precursor for the subject under study. Theflat-line-fraction signal is next analyzed to determine whether or notthere has been excessive flat-lining. This is accomplished by settingtwo thresholds: (1) “bad-fraction” where processing of the correspondingsignal is disabled if the flat-line-fraction signal exceeds thisthreshold level; and (2) “restore fraction” where processing isre-enabled if it had been previously disabled, provided theflat-line-fraction signal has crossed back below this threshold level.

To illustrate signal flat-lining, FIG. 17 has been provided. FIG. 17Aillustrates a signal containing flat data. FIG. 17A shows a signal thatexhibits a portion of flatline data. Flatline line data may occur whenthe signal exceeds voltage limits (as indicated by “flatline-clipping”)or may occur when the signal does not exceed voltage limits (asindicated by “flatline”). In the latter case, possible reasons may bethat a lead is broken or that a lead is shorted to a ground potential.FIG. 17B shows a graph of the corresponding indicator function ofinstantaneous flat-lining. FIG. 17C shows a graph of the resultingflat-line-fraction signal with a λ_(flat) parameter corresponding to a0.5 second half-life. The threshold values for “bad-fraction” and“restore good-fraction” are also shown as 0.5% and 0.35%, respectively.FIG. 17D shows the output of the flat-line detector module which is setto “1” at times when the signal is determined to have excessiveflat-lining and zero otherwise. One skilled in the art will appreciatethat many other forms of poor signal quality can be similarly definedand detected using instantaneous quality variables and weightedtime-averaging (e.g., the preferred exponential forgetting), followed byapplication of threshold criteria in order to disable and re-enablesubsequent analysis/processing utilizing the resulting longer timescalesignal quality assessments. Two very similar quality assessments arisefrom quantifying signal “clipping” or amplifier saturation at themaximum and minimum ranges, respectively, of an analog-to-digitalconverter. The fraction of time the signal spends on the maximum andminimum rails of the amplifier can be measured on any desired timescaleand incorporated into the signal quality control system.

As another example, a neurological signal parameter that may bemonitored for signal quality is a “mains” artifact, namely an excessivenoise at a certain frequency, for example, approximately 60 Hz. Such asignal may be indicative of outside noise interference (e.g., caused byturning on a light bulb) and may be indicative of a faulty orhigh-impedance electrode. Of course, the frequency may vary. Forexample, in European countries, the AC noise interference has afrequency of approximately 50 Hz . The medical device system may measureinstantaneous amplitudes of the signal and calculate a running averagefor a given moving window, of 60 seconds duration. Once it is determinedthat the average frequency or amplitude of the signal is excessive,namely above a predetermined threshold, the medical device can removethe associated electrode from consideration in the data analysis process(e.g., a seizure detection algorithm). Once the average frequency oramplitude of the signal returns below a second (typically lower)predetermined level, the associated electrode may then be brought backinto consideration.

The process for detecting excessive 60 Hz. “mains” artifact is somewhatsimilar to that used in detection of clipping. The difference is thatinstead of computing a clip-fraction signal at each point in time, anestimate of the current amplitude of the 60 Hz component in the recordedsignal is calculated, again, smoothing the estimate via exponentialforgetting. The output of this smoothing is interpreted as the average60 Hz amplitude in the most recent time window (with a time constantdetermined by the half-life associated with the 60 Hz. exponentialforgetting factor parameter). As before, the monitored neurologicalsignal is considered bad/broken (disabling analysis of the signal) ifthe average 60 Hz. amplitude exceeds one threshold and is laterconsidered good/fixed if it returns back below the same or a differentthreshold.

The instantaneous estimate of the amplitude of the 60 Hz. component ofthe input signal is obtained by first applying a digital filter to theinput signal to remove energy from frequencies other than 60 Hz, leavingonly the 60 Hz component of the received signal. This may be done usinga 3 tap FIR filter that is the complement of a 60 Hz notch filter. Thisfilter may not be sufficiently sharp in the frequency domain and mayincorrectly detect, for example, a high frequency component present in aseizure as 60 Hz and disable processing of the signal (and thus misspotential detection of the nervous system disorder). Accordingly, an IIRfilter or a longer FIR filter may be utilized to obtain additionalspecificity in the frequency domain. A similar approach may be taken toproduce a 50 Hz. artifact detector (for example, for use in Europe).

After the 60 Hz component of the input signal has been extracted, theamplitude of the wave is estimated by computing the square root of twicethe four point moving average of the square of the filtered signal. Thereason behind this is easily derived from trigonometry and omitted here.The instantaneous estimate of 60 Hz amplitude may have a relativelylarge variance due in part to the fact that the estimates are eachcompletely derived from four data points of information. The sequence ofinstantaneous estimate is therefore smoothed, utilizing exponentialforgetting for memory and computational efficiency, to produce a signal,“amp60est,” that tracks the time-varying amplitude of 60 Hz noisepresent in the signal. The amp60est signal is interpreted as atime-average of this noise over a recent time window (the length of thewindow may be determined by the half-life of the exponential forgettingparameter, λ₆₀).

The amp60est signal is next analyzed to determine whether or not therehas been excessive 60 Hz noise in the recent signal. This isaccomplished by setting two thresholds: (1) “amp60bad” where processingof the corresponding signal is disabled; and (2) “amp60restore_good”where processing is re-enabled if it had been disabled, providedamp60est has crossed back below this threshold level.

Other than an IIR or FIR filter, an alpha detector may be created byusing a filter in the 8-13 Hz band. In this event, it may be desirableto ensure that the approach used to estimate the amplitude of a singlefrequency sine wave was adapted or verified to work for estimating totalpower of the portion of signal in the particular frequency band, andthat the resulting system was validated against expert visual analysis.The use of this filter is restricted to non-epileptogenic regions so asnot to impair the process of seizure detection or quantification since8-13 Hz signal components may be present in seizures.

FIG. 18A shows a 10-second segment of ECoG data (with a seizurebeginning at t=5 seconds). FIG. 18B illustrates the same ECoG signalsegment, but contaminated by an excessive 60 Hz artifact (0.1 mV noise,beginning at t=2 seconds and ending at t=7 seconds). FIG. 18Cillustrates the 60 Hz component of the noisy signal, extracted via the 3pt. FIR filter with coefficients [0.5, 0, −0.5] (using a MATLAB filterconvention for coefficient ordering and sign). FIG. 18D shows a graph ofthe instantaneous (i.e., 4-point) estimate of 60 Hz. amplitude (1805),along with the resulting “amp60est” signal (1810), computed using theparameter 40=0.9942 (corresponding to a 0.5 second half-life at samplingrate Fs=240 Hz). The threshold values for “amp60bad” and“amp60restore_good” are also annotated as 0.075 mV and 0.035 mV,respectively. FIG. 18E shows the output of the 60 Hz. artifact detectormodule, which is set to one at times when the signal is determined tohave excessive 60 Hz. noise and zero otherwise.

It will be appreciated that the clipping artifact and the 60 Hz artifactare examples of signal quality that can be monitored. Still otherfeatures of the recorded signal, such as signal wave shape (e.g.,smoothness indexes), may also be considered. Processing of signal waveshapes would determine artifactual waves unlike those generated by thebrain such as portions of a signal with straight lines, sharp angles, orlacking any variability.

For example, poor signal quality may be determined when one signal issignificantly different from another signal in some respect, but whereinthe corresponding electrodes are physically near each other. In thisexample, power may be calculated and a poor signal may be the cause ifone signal exceeds the bounds for what is expected for signals fromadjacent electrodes, or if the absolute-value of the correlationcoefficient between the two signals is low, when it would, by thespatial closeness of the electrodes, be expected to be higher.

A maximal amount of poor quality data that is tolerable may be qualifiedusing different criterion. Poor quality data may be gauged by a signalpower to noise power ratio (S/N) that is associated with neurologicaldata. Also, poor quality data may be gauged by a fraction of theforeground window that contains a noisy signal. Typically, theforeground window is more vulnerable to noise than the background windowsince the foreground is determined over a shorter time duration. One mayalso consider different artifacts. Movement artifacts may be detectedwith accelerometers, in which corresponding outputs may be used toreduce or even cancel the movement artifacts. Other types of artifactsthat may be considered include EKG artifacts and disconnectionartifacts. EKG artifacts, when recorded from intracranial electrodes,are an indication of high impedance. Disconnection artifacts may beidentified by stationary noise in one lead or a set of leads. Thecharacteristics of a baseline that are associated with neurological datamay assist in identifying a cause of poor quality data. For example, aflat line without a shift in the baseline and without noise may beindicative that an amplifier has been deactivated or has failed.

Screening Techniques for Management of a Nervous System Disorder

The medical device system may have a mode of operation for performingscreening of a nervous system disorder. The system may thereby makedecisions about the patient's options for management of the nervoussystem disorder. For clarity, the following discussion is provided inthe context of the external system 100, although other embodiments arepossible. In the discussion, a neurological signal may be an EEG signalthat is sensed by one or more monitoring electrodes. However, in otherembodiments of the invention, a neurological signal may be providedusing other types of monitoring elements such as one or more sensors.Treatment therapies may include any number of possibilities alone or incombination including, for example, electrical stimulation, magneticstimulation, drug infusion, and/or brain temperature control. Duringscreening, the medical device system may perform various operations. Forexample, in the embodiment of treating a seizure disorder, the systemmay identify a patient's seizure focus or foci by determining portionsof the brain that are associated with a seizure. In general, theneurological event focus is the anatomic location where the neurologicalevent originates. Accordingly, the medical device system may identifyelectrode placement that may provide effective therapy and providerecommendations on which sensing and stimulation combinations are moreeffective than other sensing and stimulation combinations. Therecommendations may utilize a focal therapy assessment that is effectiveif the extent of electrographic spread is contained in the seizure focusand does not spread elsewhere (i.e., partial seizures). Alternatively,the recommendation may utilize a remote therapy assessment that iseffective if seizure related electrographic activity originates in theseizure focus and propagates to other brain regions (i.e., secondarilygeneralized seizure types). Moreover, the medical device system mayassess whether closed-loop therapy will be effective.

FIG. 19 shows a simulated EEG waveform 1901, designating an onset of aneurological event. A time event 1903 corresponds to an investigatortime of electrographic onset (ITEO), in which a clinician observessignificant electrographic activity that may predict a neurologicalevent such as a seizure. (However, a neurological event may not followtime event 1903 in some cases.) A time event 1905 corresponds to analgorithm detection time (ADT), in which a detection algorithm ofexternal system 100 detects an occurrence of a neurological event. Inthe embodiment, as is discussed in the context of FIG. 20, the ITEO andthe ADT are compared. The difference between the ITEO and the ADTprovides a measure of the detection algorithm's delay of detection fordetecting a neurological event. In general, it is desired for the delayof detection to be as small as possible with sufficient accuracy ofpredicting the neurological event. In order to accurately determine thedifference between the ITEO and the ADT, associated clocks should besufficiently synchronized (as is discussed in the context of FIG. 15).

A time event 1907 corresponds to a clinical behavior onset time (CBOT),in which a patient manifests the symptoms of the neurological event(such as demonstrating the physical characteristics of a seizure). (Insome cases, the patient may not manifest the symptoms even though anITEO occurs.) Typically, if monitoring elements (such as electrodes) areappropriately positioned, the CBOT will occur after the ITEO. However,if the electrodes are placed away from a point of the neurologicalevent, the CBOT may occur before the ITEO because of a delay of theneurological signals propagating through different portions of thepatient's brain. A time event 1909 corresponds to an investigatorseizure electrographic termination time, in which the electrographicactivity sufficiently decreases. As depicted, clinical seizure duration1911 extends between the CBOT 1907 and ISETT 1909.

To illustrate an embodiment of a screening procedure for a particularnervous system disorder, FIGS. 20 and 21 show flow diagrams for aseizure screening process to define treatment therapy according to anembodiment of the invention. Process 2000 comprises a baseline algorithmmonitoring sub-process 2003 (comprising steps 2005-2049) and a trialscreening sub-process 2151 (comprising steps 2153-2179). As depicted,the process 2000 starts at step 2001 and in step 2002, a physicianimplants electrodes into a patient in order to conduct process 2000.

In step 2005, a medical device system processes neurological signals(e.g., EEG inputs) that are sensed by the implanted electrodes. If adetection algorithm (that is utilized by the medical device system)detects a seizure in step 2007, a location of the seizure, ascharacterized by a seizure focus location, is determined by step 2009and comprising sub-step 2011 and sub-step 2017. (In the discussion,herein, a “step” may comprise a plurality of “sub-steps.” For example,when performing step 2009, process 2000 performs both sub-steps 2011 and2017. One should not construe sub-steps 2011 and 2017 as being distinctfrom step 2009.) In sub-step 2011, the medical device system identifiesthe seizure focus location by identifying the channel(s) (correspondingto an implanted electrode) with the earliest onset in which apredetermined ratio threshold is crossed. In some neurological events,several foci may be identified. Also, a patient may experience aplurality of neurological events, which are associated with differentfoci. In sub-step 2017, the medical device system reports at least oneseizure onset location through an output device.

In step 2019 (comprising sub-step 2021 and sub-step 2025) an extent ofthe seizure's spread, intensity, and duration are determined. Insub-step 2021, the medical device system identifies channels that are“involved” in the seizure in order to determine an electrographic spreadof the seizure. (A criterion for channel involvement and electrographicspread is discussed in the context of FIG. 22.) The medical devicesystem reports the electrographic spread, intensity, and duration to thephysician in sub-step 2025. The medical device system may present agraphical representation of the patient's brain to an output device forthe physician's viewing. The graphical representation highlights thelocation and the extent of the seizure focus (such as by distinguishingthe portion of the brain that is neurologically involved in the seizurewith a different color from other portions of the brain.) The graphicalimage may be three dimensional and may present a sequence of images as afunction of time, where each image represents the portion of the braininvolved in the seizure at that particular point in time (seizureanimation). Such a sequence of images graphically displays the manner inwhich the seizure spreads through the patient's brain from onset untiltermination (seizure animation).

In step 2027, which comprises sub-steps 2029 and 2031, the correctnessof electrode placement for seizure detection is verified. In sub-step2029, the ITEO (investigator time of electrographic onset correspondingto time event 1903 in FIG. 19) and the CBOT (clinical behavior onsettime corresponding to time event 1907 in FIG. 19) are provided to themedical device system. (In the embodiment, step 2027 is optional so thatthe clinician need not provide ITEO and CBOT to the medical devicesystem.) In sub-step 2031, the medical device system determines if theITEO did not occur after the CBOT. In the embodiment, the fact that theCBOT occurs before the ITEO is indicative that the selected electrodesare not sufficiently near the focus. In such a case, it can bedetermined to stop screening so that the process may end. Otherwise,step 2002 allows the physician to reposition subdural and/or DBSelectrodes. The baseline algorithm monitoring sub-process 2003 is thenrepeated.

If sub-step 2031 determines that the ITEO does not occur after the CBOT,step 2033 is executed, in which a localization accuracy and speed ofdetection are determined. Step 2033 comprises sub-steps 2035, 2037, and2039. (In the embodiment, step 2033 is optional so that the clinicianneed not provide ITEO and CBOT to the medical device system.) Insub-step 2035, a spatial difference is determined between a ADT onsetchannel (i.e., the channel that the detection algorithm associates withthe onset of the seizure) and a ITEO onset channel (i.e., the channelthat is first associated with neurological activity as determinedthrough visual analysis). While the ADT onset channel may be differentthan the ITEO onset channel, an event of the ADT onset channel and theITEO onset channel being the same is indicative of localizationaccuracy. In sub-step 2037, the medical device system reports thespatial difference and whether the spatial difference exceeds apredetermined limit. The spatial difference exceeding the predeterminedlimit may be indicative that algorithm adaptation should be executed asin step 2041. In addition, in step 2039, a measure of the algorithm'sdetection delay is determined by calculating the difference between thetimes associated with the ADT and the ITEO. If the detection delay issufficiently large, algorithm adaptation may be executed in step 2041.

Step 2041 determines whether to adapt the detection algorithm. If thedetection algorithm is not adapted, step 2048, as describer later, isnext executed. If the detection algorithm is adapted, step 2043 enablesthe physician to provide a training set (e.g., cluster data for previousseizures) so that the detection algorithm may enhance performance byadjusting its parameters. The use of filter adaptation for detectingseizures is disclosed in U.S. Pat. No. 5,995,868 entitled “System forthe Prediction, Rapid Detection, Warning, Prevention, or Control ofChanges in Activity States in the Brain of a Subject” and isincorporated herein in its entirety. In sub-step 2043, the physicianidentifies collected neurological data that characterizes the seizure(e.g., one or more detection clusters that are associated with theseizure). The detection algorithm may be adapted using differentmethods, as requested by the physician or automatically (unsupervisedlearning). With one variation of the embodiment, the detectionalgorithm, in step 2045, is adapted by adjusting threshold and timeduration settings in order to approximately optimize seizure detectionin relation to the data identified in sub-step 2043. In step 2047, thephysician evaluates the adaptation results. In step 2047, if theadaptation is satisfactory, the physician may accept recommendedsettings through an input device in step 2047. However, if theadaptation is not satisfactory, as determined in step 2047, thephysician may reject the recommended settings. In step 2048, it isdetermined whether to record more seizures. If more seizures need to berecorded, baseline algorithm monitoring sub-process 2005 continues toexecute for subsequent seizures. Otherwise, process 2000 proceeds totrial screening sub-process 2151.

In a variation of the embodiment, user interaction may be reduced oreven eliminated in some or all of the steps. For example, in steps2045-2047, a set of predetermined criteria may be used in order todetermine whether adaptation is satisfactory. Criteria may include speedof detection and detection falsing. Thus, the degree of automation maybe increased or decreased for process 2000.

In step 2153, the physician inputs an electrode configuration inaccordance with the electrographic spread and the seizure focus locationthat is presented to the physician in steps 2017 and 2025. In anotherembodiment of the invention, the medical device system provides arecommendation of the electrode configuration to the physician inaccordance to the electrographic spread and the seizure focus location.The physician may accept, reject, or modify the recommendation. A“perform_manual_stimulation” step 2155 comprises sub-steps 2159 and2161. The physician defines electrode polarities and stimulationparameters. (In an embodiment, an electrode polarity may be classifiedas a stimulation parameter. Also, some embodiments may utilize the canor case of the medical device as one or more electrodes (or as contacts)for recording and/or stimulation purposes.) Programmer 109 may providesuggested values based on the location of the electrodes and anhistorical compilation of values that have been accumulated through theevaluation of many patients. In sub-step 2159, therapy is administeredby delivering stimulation to the patient. If the physician notes anyadverse reaction to the treatment, the physician inputs an indication tothe medical device system in sub-step 2161 indicating correspondingsymptoms or changes that the patient shows. The medical device systemqueries the physician whether to modify any of the therapy parameters instep 2163 and the electrode configuration in step 2165. Step 2155 andsub-steps 2159 and 2161 are repeated if different stimulation parametersare tried in response to step 2163. Step 2153 is repeated if differentelectrodes are tried in response to step 2165. Alternatively, themedical device can recommend changes to the parameters.

If the physician indicates that the stimulation settings and theelectrode configuration should be used, the medical device systemapplies treatment in step 2167. The medical device system or thephysician determines whether the therapy is considered successful instep 2168 by a set of criteria. In the embodiment, the medical devicesystem determines if there is a sufficient reduction of a detectedfrequency, duration, intensity, and extent of the electrographic spreadthat are associated with the seizure.

If the therapy is not deemed successful in step 2168, algorithmadaptation may be performed in step 2170. Step 2170 essentiallyfunctions as in step 2041. If step 2170 determines that algorithmadaptation shall not be performed, step 2171 is next executed.Otherwise, step 2172 determines whether algorithm parameters shall bechanged. If so, step 2167 is executed; otherwise, step 2171 is executed.In step 2171, the electrodes may be reconfigured and step 2153 may berepeated. In a variation of the embodiment, restimulation of electrodesmay be expanded to electrodes that are involved in the seizure otherthan the first or second electrode as determined in sub-step 2021. Ifsubsequent trial screening shall not try different electrodes orstimulation settings (as determined by the physician), sub-process 2151is completed and the electrodes may be explanted. If the therapy isdeemed successful in step 2168, sub-process 2151 is completed and thetrial ends in step 2173 and the leads are internalized and the IPGimplanted in step 2175.

In step 2168, the medical device system may compare the detectedfrequency, duration, intensity, and extent of the electrographic spreadthat is collected during baseline monitoring algorithm sub-process 2003(as shown in FIG. 20) with the corresponding results that are collectedduring trial screening sub-process 2151. However, with stimulation,which is associated with trial screening sub-process 2151 but not withbaseline monitoring algorithm sub-process 2003, blanking is generatedduring different time intervals, in which data is not collected becauseof signal artifacts. (Further detail is presented in the context of FIG.23.) During intervals of blanking, corresponding data (which may beassociated with neurological signals provided by electrodes beingblanked and by adjacent electrodes) is not compared between baselinemonitoring algorithm sub-process 2003 and trial screening sub-process2151. If the difference between corresponding data, with and withoutstimulation, is sufficiently large (e.g., the difference is greater thanan efficacy requirement), then the therapy is determined to besuccessful.

In other embodiments of the invention, in step 2169, a physician mayevaluate a reason for the therapy deemed as not being successful.Consequently, the physician may instruct external system 100 to performalgorithm adaptation in step 2170. Alternatively, the physician mayinstruct external system 100 to bypass step 2170 and to perform step2171, in which the electrodes are reconfigured.

With a variation of the embodiment, the medical device system may applystimulation every n^(th) block of detection clusters and/or every n^(th)detection cluster (which is discussed in the context of FIG. 22) duringtrial screening sub-process 2151. Corresponding data (e.g., detectedfrequency, duration, intensity, and extent of the electrographicalspread) is collected for both detection clusters, in which stimulationis applied, and detection clusters, in which stimulation is not applied.Because blanking is generated during different time intervals whenstimulation occurs, corresponding data is not collected during thecorresponding time intervals for detection clusters, in whichstimulation is not applied so that the efficacy of the therapy can beevaluated. If the difference between corresponding data, with andwithout stimulation, is sufficiently large (e.g., the difference isgreater than an efficacy threshold), then the therapy is determined tobe successful.

Other embodiments of the invention may support other types treatmenttherapy such as magnetic stimulation, drug infusion, and braintemperature control, in which the efficacy of therapy may be evaluatedby comparing corresponding data between baseline algorithm monitoringsub-process 2003 and trial screening sub-process 2151 or by comparingcorresponding data between detection clusters, in which treatmenttherapy is applied, and detection cluster clusters, in which treatmenttherapy is not applied.

Configuring and Testing Treatment Therapy Parameters

The medical device system may have a “manual” treatment therapy modethat is different from a normal run mode (automated mode), in thatstimulations may be delivered by the user, in order to test the clinicalefficacy and tolerability of therapy configurations. In the manualtreatment therapy mode, the medical device system may enable the user toselect parameters (i.e., intensity, frequency, and pulse width), therapyelement configurations, to assess charge density, to test treatmenttherapy levels, to insure safety to the patient, and to determineefficacy and tolerability. With parameter selection, the medical devicesystem enables the user to define one or more treatment therapyconfigurations having associations with a combination of treatmenttherapy parameters. For clarity, the following discussion is provided inthe context of the external system 100, although other embodiments arepossible. In the discussion, a neurological signal may be an EEG signalthat is sensed by one or more monitoring electrodes. However, in otherembodiments of the invention, a neurological signal may be providedusing other types of monitoring elements such as one or more sensors.Treatment therapies may include any number of possibilities alone or incombination including, for example, electrical stimulation, magneticstimulation, drug infusion, and/or brain temperature control. In theembodiment where treatment therapy is electrical stimulation, theparameters may include, for example without limitation, duration ofstimulation, intensity (in volts or amps), pulse width, stimulationfrequency, pulse shape etc. With drug infusion therapy, parametersinclude a drug type, a drug dosage, at least one infusion site, infusionrate, and a time of delivering the drug dosage. The user may save eachtested treatment therapy configuration and may identify eachconfiguration by a specified name. In the embodiment, the medical devicesystem verifies that the specified name is associated with a uniqueconfiguration so that two different names are not associated with thesame configuration.

Before a user-defined treatment therapy configuration is even tested orstored, the medical device system preferably performs a charge densitycheck. For example, in the embodiment of electrical stimulation therapy,the medical device system computes the charge density of the stimulationconfiguration using the impedance of the electrode configuration,voltage level, stimulation pulse width and contact geometry of theelectrode configuration. The charge density may be computed using thefollowing formula:(I·Δw)/(surface area of electrode)where I is the current of the stimulation pulse and is approximatelyequal to the voltage level divided by the impedance, and Δw is the pulsewidth. If the calculated charge density exceeds a preset threshold, themedical device system considers the stimulation configuration to be notvalid and prevents and/or warns the user from testing with theassociated stimulation configuration. In a preferred embodiment, thepreset threshold is approximately 30 μcoulombs/cm²/phase and can be inthe range of up to 500 μcoulombs/cm²/phase. The preset threshold can beprogrammable and, of course, may vary depending on the nervous systemdisorder being treated and/or the medical device system. For example,U.S. patent application Ser. No. 10/099,436, Goetz et al., “AutomatedImpedance Measurement of an Implantable Medical Device,” and filed onMar. 15, 2002(now U.S. Pat. No. 6,978,171) discloses apparatus andmethod for automating impedance measurements of sets of electrodes thatare associated with a lead of an implanted device. Alternatively, inanother embodiment, the current may be measured directly for eachelectrode and the value may be used to compute the charge density.

The medical device system may also ensure other efficacy criteria aresatisfied for any user-defined treatment therapy configuration. Forexample, the medical device system providing stimulation therapy mayensure that the polarities of the stimulation pulses are properlydefined, e.g., all polarities cannot be off and that the voltage levelis greater than zero on at least one stimulation channel, and that atleast one cathode and at least one anode are configured.

If a treatment therapy configuration is within a permissible chargedensity range, the medical device system allows the user to test thetreatment therapy configuration. During a test, the user is able to usea start/stop delivery capability of the medical device system. If thedelivery is not terminated by the user, the medical device systemcontinues to deliver treatment therapy for the specified time duration.The medical device system then queries the user whether or not thetreatment therapy configuration was acceptable. The user (e.g., patientor physician care-giver) responds with a “yes” or “no” throughprogrammer 109. In other embodiments, a treatment therapy level that isbeyond the point at which the user stops delivery is considered as nottolerated by the patient. Moreover, the medical device system may insurethat the treatment therapy configuration corresponds to a treatment thatis safe to the patient, where the treatment therapy configuration iswithin a configuration range of safety. Safety to the patient is gaugedby an expectation that the treatment does not diminish the health of thepatient.

In the embodiment where the medical device system is providing treatmentof seizure disorders, the medical device system operates seizuredetection algorithm 800 in real-time during the manual stimulation mode,as in the normal run mode, but with detection-triggered stimulationdisabled. When selecting stimulation parameters for therapy use, themedical device system allows the user to select from a list ofstimulation parameter configurations, which have been previously definedand tested in the manual stimulation mode. The user may be restricted toselecting only those stimulation configurations that were tolerated bythe subject during testing in the manual stimulation mode. Onceconfigured, the medical device system may return to normal mode ofoperation to provide detection-triggered stimulation in accordance withthe stimulation configuration set by the users.

Clustering of Recorded Patient Neurological Activity to Determine Lengthof a Neurological Event

EEG activity, as monitored with a seizure detection algorithm, during aneurological event (such as a seizure) may result in multipleclosely-spaced detections that the user may wish to interpret as beingpart of one event (seizure), and which, if considered as separateevents, may result in an unnecessary or even unsafe number oftreatments. This may be particularly true at the beginning or end of aneurological event time when oscillations around the detection thresholdmay result in multiple closely-spaced detections, which may complicateoperations and logging of events. For clarity, the following discussionis provided in the context of the external system 100, although otherembodiments (as with a hybrid or an internal system) are possible. Inthe discussion, a neurological signal may be an EEG signal that issensed by one or more monitoring electrodes. However, in otherembodiments of the invention, a neurological signal may be providedusing other types of monitoring elements such as chemical or thermalsensors. Treatment therapies may include any number of possibilitiesalone or in combination including, for example, electrical stimulation,magnetic stimulation, drug infusion, and/or brain temperature control. Amedical device system, e.g., external system 100, determines detectionclusters using a temporal criterion, based on the distributions ofdurations of ictal discontinuities or interictal time intervals.Detections that are separated in time by a programmable inter-detectioninterval are assigned to the same cluster unit and deemed as being partof the same seizure as shown in FIG. 22. Clustering parameters areprogrammable.

FIG. 22 shows data 2201 associated with a maximal ratio 2203 asdetermined by seizure detection algorithm 800 for quantifying a seizure,as represented by a detection cluster 2205. Data 2201 (as obtained froma loop recording generated by bedside device 107) shows maximal ratio2203 from 5 seconds before an onset of detection cluster 2205 to 12seconds after the end of detection cluster 2205. (Other embodiments ofthe invention may determine the occurrence or other types of aneurological event that is associated with a nervous system disorder.)Maximal ratio 2203 (for a given instant in time) is determined from awaveform frame 2251 by identifying an EEG waveform (2253, 2255, 2257, or2259) having the largest ratio at the given instant in time. (The ratioof an EEG waveform at a given time is the largest ratio of a short-termvalue (foreground) divided by a long-term value (background) over a setof neurological signals or channels. (A short-term value or a long-termvalue may be an average value, a median value, or some other statisticalmeasure.) In the embodiment, the short-term value spans the previous 2seconds of EEG data, and the long-term value spans the previous 30minutes or more. Other embodiments may use short-term and long-termvalues spanning different time durations.) For example, at a timeapproximately equal to 10 seconds, waveform 2259 is associated with thelargest ratio, and that ratio is used to construct data 2201 at the samepoint in time. A predetermined threshold 2211 sets a minimum threshold(equal 22.0) for detecting seizure activity. However, a time constraintis predefined such that if data 2201 falls below predetermined threshold2211 and subsequently raises above predetermined threshold 2211 within atime constraint 2215 (which corresponds to 60 seconds in FIG. 22), thenthat subsequent portion of data 2201 is considered to be part ofdetection cluster 2205. Thus, detection cluster 2205 equals duration2207 plus the measured time d1 plus duration 2209. Cluster intensity isdetermined by the largest maximal ratio during detection cluster 2205.

In the embodiment, the content of abnormal or of signal of interest inan ensemble of neurological signals and the prespecified threshold valuedetermines an onset of detection cluster 2205. In such a case, if the“seizure content” of an ensemble of neurological signals is abovepredetermined threshold 2211 for a minimum time duration, seizuredetection algorithm 800 determines that detection cluster 2205 hasbegun. However, in other embodiments of the invention, seizure detectionalgorithm 800 may determine that detection cluster 2205 has occurredonly if the neurological signal that first crossed predeterminedthreshold 2211 stays above predetermined threshold 2211 for the minimumtime duration.

Other embodiments of the invention may use another measure other than aratio to determine an occurrence of detection cluster 2205. Othermeasures include an amplitude of a neurological signal and a magnitudeof a frequency spectrum component (as may be measured by a Fouriertransform) of a neurological signal.

Waveform 2253, 2255, 2257, or 2259 is considered “involved” in a seizureif the corresponding ratio equals or exceeds predetermined threshold2211. (In FIG. 22, the time duration constraint equals approximately0.84 seconds.) Each waveform in FIG. 22 is associated with a differentelectrode. In waveform frame 2251, waveforms 2257 and 2259, whichcorrespond to adjacent electrodes, are “involved” in the seizure, andthus the extent of an electrographic spread is two electrodes.

In the embodiment, external system 100 may numerically indicate theelectrographic spread, as is illustrated in the example above. Moreover,a variation of the embodiment may visually indicate the electrographicspread by distinguishing an electrode corresponding to a waveform thatis “involved” in the seizure. In the variation of the embodiment, agraphical representation of the patient's brain is shown with theelectrodes that are associated with the seizure as distinguished by acolor (such as red). Alternatively, the electrodes that are notassociated with the seizure may be distinguished by some other color.While determining extent of spread requires an application of clusteringrules, it may be more appropriate to treat spread separately.

As will be discussed in the context of the control of treatment therapy,external system 100 may use detection clustering for purposes ofdelivery and a termination of stimulation or restimulation.

External system 100 may use information about detection clusters inorder to identify circadian or other seizure trends. (Other embodimentsof the invention may support other systems for treatment of a nervoussystem disorder, e.g., hybrid system 1000 and implanted system 10.)External system 100 may determine whether seizure-related activityoccurs at specific times of the day (e.g., morning, afternoon, or night,and/or at drug administration times) or, in the embodiment of animplanted system, month (e.g., menstrual cycle). Also, external system100 may determine whether there is a cyclical pattern that is associatedwith a patient's seizures as measured by alternating periods ofseizure-dense and seizure-free time intervals. External system 100 mayidentify specific times when intense seizure activity (e.g., seizureclustering) significantly deviates from the patient's mean or medianseizure frequency (i.e., what the patient perceives are clinicalseizures only). External system 100 may produce a seizure trendingreport that identifies whether a trend in seizure activity is present,along with graphical and summary information.

External system 100 may use information about detection clusters inorder to provide measures of an event burden. The event burden may bedetermined by different criteria such as event frequency (a number ofneurological events that a patient is experiencing during a unit oftime), number of detection clusters per unit time, detection clusterseverity (a conjoint measure of the cluster intensity, the clusterduration, and the extent of electrographic spread), and an eventseverity (as determined by a patient's classification of seizureseverity as inputted into external system 100 or ratio).

External system 100 may adjust therapy parameters, e.g., stimulationparameters or drug infusion parameters, in order to adjust to theeffectiveness of the treatment. The embodiment supports bothintra-cluster staging and inter-cluster staging. With intra-clusterstaging, external system 100 may automatically adjust a therapyparameter (e.g., the stimulation voltage or amperage) with eachsuccessive administration of stimulation within a detection clusteruntil the seizure is terminated. The therapy parameter may be a memberof a predefined set of parameters. At least one predefined set ofparameters may be configured during trial screening sub-process 2151.External system 100 may also automatically adjust therapy parameterswith each successive administration of clustered therapy (i.e.,administration or withholding of therapy based on a cluster).Inter-cluster adjustment may occur at every n^(th) detection cluster oraccording to a cluster sequence pattern (e.g., adjusting therapyparameters only on the second and seventh subsequent detectionclusters). The user may define the cluster sequence pattern using theprogrammer 109. With both intra-cluster staging and inter-clusterstaging, a predefined threshold may be established (such as by the userthrough programmer 109) that limits the adjustment of a therapyparameter.

External system 100 keeps track of seizure severity measures such asintensity, duration, and electrographic spread using measures of centraltendency or other suitable measures and ranks all detections as afunction of intensity, duration, etc, and also as a function of thetemporal evolution of these variables “within a detection cluster” and“between detection clusters”. External system 100 also sorts seizuresaccording to time of day, day of week, week of month, month of year andalso year, using the same variables described above and also accordingto their temporal evolution within and between clusters. Using thesedata, external system 100 creates another rank to track circadian,ultradian and other rhythms and features. If any detection severitymeasure exceeds a certain level, but remains below another level, e.g.,the 99% tile, one or more therapeutic parameters are increased, by apredetermined amount that may vary intra-individually (according to thesubjects seizure statistics) and inter-individually. The number andsequence in which parameters would be increased is also prespecifiedintra- and inter-individually. If the pre-specified type of and sequenceof parameter changes are not efficacious, search methods, e.g., a randomsearch method, may be used to select more efficacious therapy parametervalues, the number of therapy parameters increased at one given time andthe order in which they are increased. External system 100 is programmednot to accept stimulation parameter values that exceed a pre-specifiedcurrent density or the toleration limits as determined in step 1961.

If one or more measures of seizure activity exceed a predetermined limitat any point in time, e.g., the 99% tile value of preceding detections,the event may be an indication that therapy may have a paradoxicaleffect. In such cases, one or more of the therapy parameters may bedecreased or the external system 100 may be shut down (temporarily)while gathering more data to reassess the situation. (In the embodiment,determining whether to reassess therapy parameters may occur at any timeduring the treatment therapy.) The amount of time that therapyparameters can be modified may be based on endpoints such as quality oflife and/or statistical analysis of the data.

External system 100 may be capable of changing geometry or configurationof stimulation including the number of contacts, as well as the anodeand cathode location. For instance, if stimulation is being delivered tocontacts 1 and 3 without good results, external system may add contact 2or change to contacts 1 and 4. In addition, stimulation may be limitedto the contacts where an event was detected. These decisions are madeusing an ability of external system 100 to quantify detection intensity,duration, etc., at each contact and on the history of values at eachcontact. Also, since direction of current flow may determine theresponse to electrical stimulation, the anode and cathode may bereversed. Or to increase the size of the negative field, the anode maybe shifted to a reference electrode of lower impedance (due to largersurface area such as one of the surfaces of the case containing thestimulation engine). External system 100 has the capability ofestimating current density at any contact site. Contacts for measuringcurrent density may also be placed at certain sites at a distance fromwhere stimulation is being delivered, in a grid or some other pattern tobetter assess safety and efficacy, by obtaining a more realisticestimate than the one that is routinely obtained at the contact-braininterface.

The embodiment has described monitoring elements taking the form ofelectrodes sensing electrical activity associated with brain ECoG. Otherembodiments of the monitoring element adapted to sense an attribute of aNervous System Disorder could be used. Alternatively, the monitoringelement could detect abnormal concentrations of chemical substances inone location of the brain. Yet another form of the monitoring elementwould include a device capable of detecting nerve compound actionpotentials. The monitoring element also may take the form of a devicecapable of detecting nerve cell or axon activity that is related to thepathways at the cause of the symptom, or that reflects sensations thatare elicited by the symptom. The monitoring element may take the form ofa transducer consisting of an electrode capable of directly measuringthe amount of a particular transmitter substance or its breakdownby-products found in the interstitial space of the central nervoussystem. The level of the interstitial transmitter substance is anindicator of the relative activity of the brain region. An example ofthis type of transducer is described in the paper “Multichannelsemiconductor-based electrodes for in vivo electrochemical andelectrophysiological studies in rat CNS” by Craig G. van Home, SpencerBement, Barry J. Hoffer, and Greg A. Gerhardt, published in NeuroscienceLetters, 120 (1990) 249-252.

The monitoring element may be external to the body communicating withthe implanted portions through telemetry. An example of an externalmonitoring element is an electrical device that includes an electrodeattached to the surface of the skin that passes a small current tomeasure the skin impedance. An example of this type of the monitoringelement is described in the paper “Skin Impedance in Relation to PainThreshold Testing by Electrical Means”, by Emily E. Meuller, RobertLoeffel and Sedgwick Mead, published in J. Applied Physiology 5,746-752, 1953. A decrease in skin impedance may indicate an increase inanxiety. Other monitoring elements such as Carbon dioxide gas sensors orother sensors that can detect the physiological parameters such as thoselisted above will be clear to those skilled in the art.

Control of Treatment Therapy During Start-Up and During Operation

In an embodiment, any one of the above-described medical device systemsmay limit delivery of therapy during start-up and during operation forimproved efficacy. During start-up, the medical device system may beprogrammed to only monitor neurological signals (no delivery oftreatment therapy) for a predetermined time period (e.g., 30 minutes)after the medical device system is turned on. Of course, the time periodis a function of the background window length and may vary in duration.During this start-up period, the medical device system may collect dataand allow the seizure detection algorithm system described above tostabilize and adjust to data from the individual and set of signalsbeing monitored in order establish a background and avoid potential forerroneous detections (and unwanted administration of therapy) beforesuch information has been acquired. It has been determined that duringthe period of algorithm stabilization, the probability of false positivedetections is high. Thus, by programming the medical device system tonot provide treatment therapy during this period, unnecessary treatmenttherapies can be avoided.

To illustrate how the treatment therapy can be limited during start-up,the embodiment of an external system 100 is described. The externalsystem 100 monitors electrical brain activity in the patient andcollects data on eight electrodes (each with respect to a commonreference or in a “differential” manner) preferably at 250 Hz samplingrate. This collected data is transferred to the DSP chip (where thedetection algorithm resides) in blocks of 96 bytes, which is 8 channelsmultiplied by 12 data points per channel, and occurs at a fixed rate (48milliseconds). Time in the external system 100 can therefore be measuredin block counts. The external system 100 starts counting blocks from thestart of a session, and uses them for numerous purposes, such ascontrolling the stimulation board (found, for example, in thestimulation electronics module 203 of the bedside device 107).

Software methods within the external system 100 provide thefunctionality being described. In particular, software of the externalsystem 100 engages an authorize stimulation subroutine that checks fornumerous conditions to make sure they are all valid before authorizingstimulation. Software within the external system 100 also has a lockoutparameter that is checked by the authorize stimulation routine to makesure that enough blocks have passed to correspond to the startup timebefore it will allow stimulation. The default value that is used is37,500, which corresponds to 30 minutes. After this period, the lockoutis released and no longer prevents stimulation. Any seizure detectionsprior to this period that would otherwise result in stimulations maythereby be prevented. The time period during which the medical devicesystem only monitors and delivers no treatment therapy may beestablished by techniques other than block counts. For example, the timeperiod may be established such that a set quantity of information hasbeen obtained from the monitoring elements.

As discussed, the functionalities of the present invention may beimplemented in other embodiments. In the embodiment of a hybrid controlsystem, the aforementioned software methods may be implemented withineither the implantable device 953 or the external wearable device 1000(see FIG. 9). In the embodiment of a fully implanted control system, theaforementioned software methods may be implemented within the implanteddevice.

During its operation, the medical device system may also invoke anynumber of methods for limiting therapy during operation if it wouldresult in therapy being outside of the acceptable range for one or moretherapy parameters. For example, in the embodiment of the externalsystem 100, the system 100 may limit the total number of stimulationsdelivered for a variety of reasons including, but not limited to,programming checks and lockouts, tissue damage, and run time monitoringand control. During programming of the external system 100, theprogrammer software checks the programming information to make sure thatthe stimulation board (e.g., Synergy®) never provides a charge densityabove a predetermined limit (e.g., 30 uC/cm²/phase). In particular, theprogrammer software performs calculations based on the geometry of thelead being used and the attempted setting entered by the user. If thispredetermined limit is exceeded, a message informs or warns theuser/clinician and prevents the parameters from being sent to thestimulation board.

The programmer 109 may also limit the stimulation ON time that isallowed to be programmed into the external system 100 by calculatingparameters that will be used during run time to control the stimulationON time. Parameters include, for example and without limitation, amaximum number of stimulations per detection, a maximum number ofstimulations per cluster, a maximum stimulation ON time during aone-hour period, and a maximum stimulation ON time during a one-dayperiod. Depending on the embodiment, these parameters may be fixed inthe software or programmable so that they may be adjusted by thephysician or a qualified user.

Although the aforementioned functionality is described as existing inprogrammer 109, in other embodiments such as a hybrid control system ora fully implanted system, the functionality may reside in a physician orpatient programmer (or in the implanted device or the hybrid system).

Again, it will be appreciated that other embodiments are possibleincluding medical device systems providing other treatment therapies aswell as systems that monitor other symptoms or conditions of a nervoussystem disorder. In the embodiment, the amplitude level may be adjustedbetween 0 and 20 volts; pulse widths may be adjusted between 20microseconds to milliseconds, and the pulse frequency may be adjustedwithin 2 pps and 8,000 pps, in which the wave forms may be pulsed,symmetrical biphasic, or asymmetrical biphasic.

Timed Delay for Redelivery of Treatment Therapy

The medical device system may repeatedly administer treatment therapyduring a detection, until the symptom or condition of the nervous systemdisorder has been terminated. For clarity, the following discussion isprovided in the context of the external system 100, although otherembodiments are possible (e.g., the hybrid system). In the discussion, aneurological signal may be an EEG signal that is sensed by one or moremonitoring electrodes. However, in other embodiments of the invention, aneurological signal may be provided using other types of monitoringelements such as other types of sensors. Treatment therapies may includeany number of possibilities alone or in combination including, forexample, electrical stimulation, magnetic stimulation, drug infusion,and/or brain temperature control. The redelivery of stimulation iscontrolled by a programmable minimum interstimulus interval (min ISI).The minimum interstimulus interval begins each time the medical devicesystem terminates stimulation, and ends after the specified amount oftime. During the interstimulus interval, the device is not able to turnon stimulation.

FIG. 23 shows a timing diagram including the seizure detection algorithmprocessed maximal ratio signal. As shown, EEG signal data 2300 areprocessed by seizure detection algorithm 800. Signal data 2300 ischaracterized by a maximal ratio 2301 that is displayed as a function ofa time reference 2303 (which is relative to a detection cluster starttime in seconds). (Maximal ratio is discussed in the context of FIG. 22.The maximal ratio is the largest ratio of a set of ratios, in which eachratio is determined by a short-term value of a neurological signaldivided by the corresponding long-term value.)

Signal data 2300 comprises signal segments 2305, 2307, 2309, 2311, and2313. During segment 2305, signal data 2300 is collected, processed, andtracked by the medical device system (e.g., external system 100) inorder to determine if a seizure is occurring. As a result of the seizuredetection at the end of interval 2305, issued by the seizure detectionalgorithm's analysis of input signal data 2300 during time interval2335, the medical device delivers an electrical stimulation pulse 2315to a desired set of electrodes (e.g., electrodes 101). Other embodimentsof the invention, of course, may use other forms of therapeutictreatment discussed above.

During stimulation pulse 2315, a corresponding channel is blanked byhardware during a hardware blanking interval 2325 (approximately twoseconds in the example as shown in FIG. 23) so that no signal iscollected or analyzed during this interval of time. Additionally,meaningful data typically cannot be collected after stimulation pulse2315 for a period of time because associated amplifiers (e.g., amplifier1111) need to stabilize and because signal artifacts may occur betweenelectrodes during a amplifier recovery interval 2321 (approximatelythree seconds as shown in FIG. 23).

A software blanking interval 2329 (approximately five seconds as shownin FIG. 23) is equal to hardware interval 2325 plus amplifier/signalrecovery interval 2321. During software blanking interval 2329, themedical device system does not use signal data 2300 during segment 2307(corresponding to hardware blanking interval 2325) and segment 2309(corresponding to amplifier recovery interval 2321). In otherembodiments, the medical device system may not collect signal data 2300during software blanking interval 2329. (In the embodiment, softwareblanking may occur on a subset of all channels, including channels notbeing stimulated. Also, the set of channels that are software blankedmay be different from the set of channels that are hardware blanked.)

In the embodiment, hardware blanking interval 2325 and software blankinginterval 2329 may be predetermined, in which intervals 2325 and 2329 maybe programmable or non-programmable. Hardware blanking interval 2325 maycorrespond to blanking of software or blanking of hardware, and softwareblanking interval 2329 may correspond to blanking of hardware orblanking of software.

After software blanking interval 2329 (the end of interval 2329coincides with the end of amplifier recovery interval 2321), the medicaldevice system resumes analyzing signal data 2300 using seizure detectionalgorithm 800 during recovery interval 2323 (and produces output ratiocorresponding to segment 2311 in FIG. 23). The recovery interval 2323allows time to ensure that subsequent analysis output is able tomeaningfully represent the post-treatment brain state. Since, in theembodiment, seizure detection algorithm 800 utilizes a two-secondforeground window, the algorithm recovery interval 2323 is approximatelytwo seconds. The medical device system may then use this meaningfullyrepresentative detection algorithm output in a subsequent interval 2333in order to determine whether treatment therapy was effective or if theseizure is continuing. This subsequent interval may be an instant intime (i.e., one data point), or may be extended to acquire sufficientmeaningful data to permit a statistical analysis of the efficacy of thetherapy. This information may be used to determine whether or not toredeliver treatment therapy.

In the embodiment, the medical device collects a minimum amount ofmeaningful data (corresponding to segment 2313) during a minimummeaningful data interval 2333. This enables statistical analysis of theefficacy of the therapy. The minimum interstimulus interval is equal toamplifier recovery interval 2321 plus detection algorithm recoveryinterval 2323 plus minimum meaningful data interval 2333. If the maximalratio 2301 remains at or above a predetermined threshold 2351, then themedical device system re-applies an electrical stimulation pulse 2317 tothe desired set of electrodes. If instead, the maximal ratio is belowthe predetermined threshold 2351, the medical device system continues tomonitor signal data 2300. An electrical stimulation or other form oftherapy may be applied if a subsequent seizure detection is made by themedical device system, indicating a continuation of the seizuredetection cluster.

In the embodiment, during the combined periods of algorithm recoveryinterval 2323 and minimum meaningful data interval 2333 (correspondingto a total time of approximately 2.5 seconds), the detection algorithm'smaximum ratio 2301 is monitored to determine if the subject is in astate of seizure or not. Two different scenarios are possible, in whichdifferent rules are employed for each case to decide whetherrestimulation should occur. If the algorithm's maximum ratio 2301exceeds the predetermined threshold 2351 for the entire period (i.e.,interval 2323 plus interval 2333), stimulation will be re-administered.Onset of stimulation will occur at the end of the minimum interstimulusinterval. In such a case, the algorithm's duration constraint (timeduration), which in the embodiment is approximately equal to 0.84seconds, is not evaluated, since the subject is still in a state ofseizure detection. However, if the algorithm's maximum ratio 2301 dropsbelow the predetermined threshold 2351 during this period, the seizuredetection ends. Stimulation will not be administered until the nextseizure detection within the detection cluster. This requires that thealgorithm's threshold and duration constraints are both satisfied (i.e.,maximum ratio 2301 is as great as the predetermined threshold 2351 forthe duration constraint).

The process of restimulation will occur as long as the subject remainsin a state of seizure, and that pre-programmed stimulation safety limitshave not been reached.

The number of allowable stimulations per detection cluster is a functionof the stimulation duration and the stimulation limits. For example, inthe embodiment, a stimulation duration of 2 seconds would result in amaximum of 5 stimulations in a single seizure detection, and a maximumof 10 for the entire detection cluster. However, the clinician mayprogram the number of allowable stimulations per detection cluster inorder to adjust treatment therapy for the patient. If the subjectremains in a non-seizure state for a period of 1 minute, the clusterends. Determination of whether stimulation will be triggered for thenext detection cluster is determined by a programmed stimulationsequence.

Cycle Mode Providing Redundant Back-Up to Ensure Termination ofTreatment Therapy

The medical device system may provide a cycle mode of operation to serveas a redundant backup to ensure that therapy is stopped after apredetermined time period. In an embodiment, this functionality isprovided within an implanted therapy device such as an implantable pulsegenerator or a drug infusion device. In the embodiment of providingelectrical stimulation treatment therapy, for example, the cycle mode ofoperation may be provided in a stimulation board (e.g., the stimulationoutput circuit that is used in the Synergy® product sold by Medtronic,Inc.), which is typically within the implantable pulse generator. In thespecific embodiment of the external system 100, the stimulation boardmay reside in the stimulation electronics module 203 of the bedsidedevice 107. In general, the stimulation board of the medical devicesystem provides continuous stimulation where stimulation is turned onand remains on until it is explicitly turned OFF. A programmer or anexternal device, for example, may provide the necessary ON or OFFcommands to the stimulation board.

The stimulation board has a cycle mode, having a defined ON time, to actas a redundant back-up in case the stimulation board does not receivethe necessary command to turn OFF the stimulation therapy. The ON timemay be predefined or may be programmable by a treating physician orqualified user. More particularly, when the stimulation board receivesan ON command, the stimulation board cycles ON and delivers stimulation.The stimulation board then eventually receives an OFF command to turnoff the stimulation. If the medical device system, however, shouldhappen to fail during stimulation and be unable to supply the OFFcommand, the cycle mode acts as a redundant backup to make sure thatstimulation turns off after the ON timer has expired.

FIG. 24 is a flow chart illustrating a process for implementing a cyclemode of operation within generally any medical device system. Theprocess may be implemented as logic circuitry or firmware in the medicaldevice system. At step 2405, the medical device system receives therapyparameters for the treatment therapy. For example, in the case of astimulation device, the treatment therapy parameters may includeelectrode identification, pulse width, pulse frequency, and pulseamplitude. The information may be received by the electronics componentresponsible for providing the electrical stimulation. In the externalsystem 100, it may be the stimulation electronics module 203. In thehybrid system 1000, it may be the implantable device 1100. At step 2410,the medical device system waits until it receives an ON command. Once anON command is received, at step 2415, the medical device system starts acycle timer ON and starts delivering the treatment therapy in accordancewith the previously-received treatment therapy parameters. Once again,the cycle ON timer may be predefined or may be programmable by atreating physician or qualified user. The medical device systemcontinues to deliver treatment therapy and checks whether it received anOFF command, at step 2420, and whether the cycle ON timer has expired,at step 2425. Again, the cycle ON timer may be pre-configured to anyduration or may be programmed by the treating physician. Under normaloperation, once an OFF command is received, at step 2435, the medicaldevice system turns off the treatment therapy and turns off the cycle ONtimer. The system then returns to step 2410 to start the process overonce another ON command is received.

If, on the other hand, the system does not receive the OFF command, butthe cycle ON timer has expired, at step 2430, the system turns off thetreatment therapy and activates a cycle OFF timer. During the cycle OFFtime, the system is unable to provide treatment therapy. This providesan indication to the patient or the physician that the redundant cyclemode was activated due to failure in receiving an OFF command. The cycleOFF timer is typically pre-configured to a predetermined maximum timeduration to give the physician time to notice that the medical devicesystem is not functioning as expected. (As an example, an embodimentsets the cycle OFF timer to approximately 32 hours.)

The system next waits until either the cycle OFF timer has expired, atstep 2445, or until the system has received an OFF command, at step2440. The “YES” branch from step 2445 to step 2415 is not typicallyexecuted because the cycle OFF time should give the physician time tonotice system failure and to intervene accordingly.

Phase Shifting of Neurological Signals

In the present invention, multiple neurological signals may be processedfor information about a symptom or a condition of a nervous systemdisorder. Successful detection of a disorder is dependent on thetemporal integrity of the signals relative to one another. Consequently,once the neurological signals are sampled, they may become virtuallyshifted in time by interpolating between adjacent samples. Temporalalignment can be approximated using an interpolation phase shiftalgorithm, thereby correcting any error caused by the time shiftedneurological signals. This technique may be implemented within aclosed-loop medical device system or a medical device system having onlymonitoring.

Given N channels of data that are sampled at different points in time,interpolation techniques may be utilized to obtain estimates of truetime-locked signal values on all channels at a sequence of time points.Essentially, the interpolation techniques reconstruct an estimate ofwhat the data would have been if all channels had been simultaneouslysampled, even though they were not.

In order to accomplish this, an interpolating model is selected for usein determining signal estimates at time points between those for whichdigitization was actually performed. The particular interpolatingfunction, in general, may depend on data values received up to themoment in time it is evaluated. One channel is selected, typicallychannel #1, whose sampling times are used as a temporal reference.Estimates of the values on all other channels may then be computedrelative to the temporal reference. Knowing the elapsed time betweendigitization of the reference channel and that of any other channel, achannel-dependent time-shift, Δt_(j), may be obtained that representsthe time difference between the sampling times on channel j and thecorresponding reference times at which the estimates are desired.

In the preferred embodiment, the interpolating model is a third degreepolynomial that is fit (i.e., defined by) the most recent four datapoints on each signal channel. One skilled in the art will appreciatethat other interpolating models, e.g., lower or higher degreepolynomials, may also be used, depending on such things as spectralproperties of the raw signal being interpolated and computationalcomplexity or power requirements of the device processor.

In the preferred embodiment, the phase-corrected corrected signal hasbeen implemented in the computationally efficient form of achannel-dependent finite impulse response digital filter applied to theraw data obtained for each channel. More specifically, if the sequenceof raw data on channel j is denoted by X₁ ^(j), X₂ ^(j), X₃ ^(j), . . ., X_(k−2) ^(j), X_(k−1) ^(j), X_(k) ^(j), then the interpolated output,Y_(k) ^(j), at time sequence point k and on channel j is obtained viathe formula:

$Y_{k}^{j} = {\sum\limits_{i = 0}^{3}{b_{i}X_{k - i}^{j}}}$ where${b_{0} = {\frac{C_{j}^{3}}{6} - \frac{C_{j}^{2}}{2} + \frac{C_{j}}{3}}},{b_{1} = {{- \frac{C_{j}^{3}}{2}} + {2C_{j}^{2}} - \frac{3C_{j}}{2}}},{b_{2} = {\frac{C_{j}^{3}}{2} - \frac{5C_{j}^{2}}{2} + {3C_{j}}}},{b_{3} = {{- \frac{C_{j}^{3}}{6}} + C_{j}^{2} - \frac{11C_{j}}{6} + 1}},\text{and}$$C_{j} = {\frac{25 - j}{8}.}$

FIG. 25 shows a flow diagram 2500 for phase shifting in accordance withanother exemplary embodiment based on a polynomial interpolation model(e.g., parabolic, linear, cubic, etc.). Step 2501 initiates phaseshifting for one the received neurological signals or channels relativeto a first neurological signal, which is treated as a reference signal.In step 2502, signal samples for the received neurological signal arecollected corresponding to the current sample time and the two previoussample times. In steps 2503 and 2504, unknown variables for theinterpolation equation are calculated. In step 2505, a delta time shiftis computed for the current channel. In step 2506, the shifted sampleoutput is computed by solving the polynomial curve fit equation at thedelta time shift. The received neurological signal may thereby becorrected by shifting the signal samples in time by an amount determinedin step 2506 so the neurological signal is synchronized with the defaultneurological signal. This process may then be repeated for each receivedneurological signal. The time-shifted neurological signals and thedefault signal may thereby be utilized to provide closed-loop feedbackcontrol of the treatment therapy.

FIG. 26 illustrates an example of applying a parabolic interpolationphase shift algorithm. In this example, a simple sine wave signal fromchannel 1 is sampled, indicated by 2601, and treated as a referencesignal. A second channel is sequentially sampled and experiences a shiftin time, as indicated by 2602. Signal 2603 shows the second signalcorrected for the time shift after the phase shift algorithm is applied.In one embodiment, sequential samples from a given channel is used togenerate a parabolic curve to “interpolate” what the actual value wouldhave been had they sampled it at the correct time (all channels sampledin parallel). The accuracy may be improved by using other interpolationfunction models, including higher order polynomials. In anotherembodiment, data samples themselves may be shifted.

Those skilled in the art will appreciate that other phase shiftingalgorithms may be utilized including, in particular, other formulas fordetermining the amount of time shifting. Moreover, although described inthe context where nervous system disorder being treated is a seizure,the principles of the invention may be applied toward treatment of othernervous system disorders, and may be utilized to process any number ofneurological signals.

Channel-Selective Blanking

In accordance with another feature of the present invention, any one ofthe above-described medical device systems may be configured so that itmay provide hardware and software blanking functionality. In particular,the medical device system may invoke either hardware blanking and/orsoftware blanking of a received neurological signal if the system shouldnot process the signal for the corresponding monitoring element. In theembodiment of the external system 100, for example, hardware blanking(through blanking circuitry 401) corresponds to the system disconnectingthe EEG amplifier 103 from the channel that is being stimulated bystimulation electronics 203 through the associated electrode during atime interval that includes the stimulation delivery period. (In theembodiment, amplifier 103 is disconnected from the associated electrodeand connected to a reference voltage.) Because no data is beingcollected during stimulation, no data (for the corresponding channel) issent to the processor 207 to be processed by the detection algorithm 800at the associated time. Data may, however, be collected on otherchannels that are not being stimulated and processed at an associatedtime.

In addition, the medical device system may invoke software blanking inwhich data from a neurological signal is collected for a particularchannel, but the medical device system determines that data should notbe processed during a time interval (e.g., for use with the detectionalgorithm discussed above). For example, if hardware blanking is invokedfor certain channels, the medical device system may invoke softwareblanking on those and/or on any electrode (i.e., on different channels)where a stimulation artifact may occur. (For example, stimulating anelectrode may cause adjacent electrodes to incur stimulation artifacts.)As another example, software blanking may be invoked if the EEGamplifier 103 is recovering after the termination of stimulation on atleast one channel.

FIG. 27 shows a flow diagram (process) 2700 for closed loop therapyincluding hardware and software blanking in accordance with anembodiment of the invention where the nervous system disorder beingtreated is a seizure and the treatment therapy is electricalstimulation. Step 2701 initiates signal processing by the seizuredetection algorithm 800. In step 2703, the seizure detection algorithm800 determines whether or not a seizure has been detected. In theembodiment, an output ratio (e.g., the maximum ratio 2203 that isassociated with the waveforms 2253, 2255, 2257, or 2259 as shown in FIG.22) exceeds a predetermined threshold (e.g., the threshold 2211) forlonger than a given time duration. In the embodiment, the correspondingsignal(s) may be obtained from an electrode or from a group ofelectrodes (selected from the electrodes 101). In step 2705, a beginningof a detection cluster is recognized in accordance with the seizuredetection algorithm 800. Detection of a seizure will trigger delivery ofa treatment therapy (in this case stimulation), which may be redeliveredduring a detection cluster (e.g., cluster duration 2205) until theseizure has been terminated or safety limits (such as maximumstimulation on time per given period of time) have been reached ortolerability becomes an issue. In accordance with the determinedtreatment, step 2709 is executed, in which stimulation to a selectedelectrode or group of electrodes is applied, hardware and softwareblanking are invoked, and a stimulation timer is initiated. (In otherembodiments, the medical device system may utilize drug infusion or acombination of electrical stimulation and drug infusion to delivertreatment therapy.) When the stimulation timer has expired, asdetermined in step 2711, the stimulation pulse terminates, hardwareblanking ceases, and an interstimulus interval (ISI) timer and asoftware blanking timer are started in step 2713. Processing of thesignals is not resumed and the seizure detection algorithm's output(i.e., the output ratio and/or detection state) is held constant(throughout the hardware and software blanking periods,) until thesoftware blanking timer has expired as determined in step 2715.

While the detection algorithm is being software blanked, no recordeddata is provided to the algorithm (from the channels which are blanked)in order to avoid the simultaneously occurringhardware-blanking/reconnection artifacts and/or stimulation artifactsfrom adversely affecting the detection process. During these timeintervals, the corresponding individual channel ratios that werecomputed at the instant the software blanking began are held constantthroughout the period of software blanking. This is done to avoid, forexample, terminating an active detection by setting the ratios to somelesser value. Ratios are allowed to fluctuate as data is analyzed onother non-software-blanked channels.

Referring to FIG. 27, when the software blanking timer has expired instep 2715, the algorithm resumes processing corresponding signals instep 2717. If the output ratio remains above the predetermined threshold2211, indicating that detected activity is continuing as determined instep 2719, the ISI timer is reset and step 2721 determines if the ISItimer has expired. (The ISI timer sets a minimum ISI time betweenadjacent stimulation pulse trains.). If the ISI timer has expired,another stimulation pulse may be applied to the selected electrode orgroup of electrodes (as executed by step 2709) in accordance with thedetermined treatment therapy. (Moreover, the determined treatmenttherapy may apply subsequent stimulation pulses that are separated by atime greater than the minimum ISI time.) If the ISI timer has notexpired, step 2719 is repeated. In step 2719, if the seizure detectionalgorithm 800 determines that the output ratio drops below thepredetermined threshold 2211, a cluster timer (corresponding to the timethreshold 2215 in FIG. 22) is initiated in step 2723. The cluster timeris also reset in step 2723

If the cluster timer (e.g., corresponding to time threshold 2215) hasexpired, as determined by step 2725 after reaching step 2723, the end ofthe detection cluster is recognized in step 2729 and data that iscollected during the cluster duration, as well as some prior period ofdata that may be of interest, may be stored in a loop recording (e.g.,SRAM and flash memory 605) in step 2731. (The expiration of the clustertimer is indicative of a maximum time duration that the output thresholdcan be below a predetermined threshold, e.g., predetermined threshold2211, while the detection cluster is occurring. In other words, if theoutput threshold is below the predetermined threshold and the clustertimer expires, process 2700 determines that the detection cluster hasended.) Step 2701 is then repeated. A subsequent detection cluster mayoccur during the seizure, causing steps 2705-2731 to be repeated.

If the cluster timer has not expired, as determined by step 2725,seizure detection is performed in step 2727, as was performed in step2703. Step 2727 determines if the detection cluster associated with theseizure, as detected in step 2703, continues due to a new seizuredetection that occurs before the cluster timer expires. If so, the ISItimer is reset and step 2728 determines if the ISI timer has expired. Ifso (i.e., the detection cluster continues and the time between adjacentstimulation pulses is greater than the ISI minimum time), then step 2707is repeated. If the ISI timer has not expired, then step 2727 isrepeated. If step 2727 determines that a seizure is not detected, step2725 is repeated.

Steps 2701 through 2731, as shown in FIG. 27, may be sequentiallyexecuted. However, in a variation of the embodiment some of the stepsmay be executed in parallel while other steps may be sequentiallyexecuted. For example, step 2701 (start/continue signal processing) maybe executed in parallel with step 2731 (data storage).

Hardware and/or software blanking may be automatically applied basedupon the results of applying signal quality control algorithms, such asthose described above, to test the reliability of sensor signals.Application of signal quality control may at anytime result incontinuous hardware or software blanking of a particular sensor due toartifact. However, signal quality control algorithms may also be appliedto any of the sensor channels to determine if the applied therapy (e.g.,stimulation) is causing artifacts that require hardware or softwareblanking during and after application of the therapy. Those sensorchannels determined not to by affected by the application of thetreatment therapy do not need to be blanked, thus enhancing the abilityof the system to monitor the patient. In addition, periodic checking ofa sensor channel following a treatment pulse and applying signal qualityalgorithms can automatically determine the length of time needed forhardware and/or software blanking for that channel during futureapplications of the therapy. For example, a signal that is associatedwith an electrode in proximity of a stimulated electrode may be analyzedto have artifact characteristics, including during a time interval inwhich an artifact affects the signal. Alternatively, parameters of thetherapy treatment may be adjusted within a range of values known to betherapeutic in an effort to reduce the effect on the signal quality ofadjacent sensors. In this manner the medical device system can enhanceits ability to collect data while providing treatment therapy.

Even though hardware blanking is generated during the time interval inwhich an electrode is being stimulated, hardware blanking may begenerated for other time intervals in which the associated amplifier mayexperience saturation (clipping or overload). The need for softwareblanking may be determined from geometric and electrical configurationsof the electrodes, e.g., distance between electrodes and the stimulationintensity that may be measured by stimulation voltage). For example, theinducement of artifacts on a channel may be inversely related to thecorresponding electrode and the stimulated electrode.

In an embodiment, software blanking may be determined by a calibrationprocess in which an electrode is stimulated and the correspondingartifacts are measured for adjacent electrodes. For example, an artifactmay be determined by stimulating a first electrode and measuring theartifact on a channel of a second electrode. The artifact may bedetermined by measuring a signal perturbation on the channel withrespect to the signal on the channel without stimulating the firstelectrode. The procedure may be repeated by individually stimulatingother electrodes. Alternatively, an impedance of the second electrodemay be measured while stimulating the first electrode to determine aneffect on the measured impedance.

Multi-Modal and Long-Term Ambulatory

The medical device system may support multi-modal operation, in whichoperation is modified in accordance with the configuration of themedical device system. One skilled in the art will recognize that theimplanted system 10 (as shown in FIG. 12) may be more limited infunctionality and features when compared to hybrid system (e.g., asshown in FIG. 9) due to the need to conserve the limited amount ofenergy available in a totally implanted system. One approach toexpanding the capabilities is to incorporate a rechargeable battery inthe implanted system 10. An alternative approach is to partition some ofthe features, particularly those that consume the most energy and arenot used all the time, to the external portion 950 of a hybrid system oranother external component of a hybrid system that may be associatedwith a process step, e.g., step 2025 of process 2000 as described inFIG. 20. (As previously discussed, FIG. 10 shows an exemplary embodimentof the external portion 950 with the associated programmer 1021.) In oneembodiment, the medical device system operates as a closed-loopstimulator, in which stimulation therapy is adjusted in accordance withsignal measurements and analysis that is performed. The electronics andsoftware required to operate the closed loop control reside in theexternal portion 950 of a hybrid system or the external wearable signalprocessor 1425 in a hybrid system with a telemetry booster stage orrelaying module. However, if the closed-loop control is removed from theconfiguration, corresponding to a removal of the external portion 950,the medical device system may operate as an open loop stimulator, inwhich electrodes with associated implanted electronics generatestimulation therapy. An external component may be removed for differentreasons. For example, the external portion 950 may be removed at timeswhen monitoring or when therapy features executed by the externalportion are not required, such as at night, for patients who do notrequire closed-loop control while sleeping. Also, an external componentmay be configured in a medical device system in order to invoke optionalfunctionality or enhanced functionality, in which the external componentinteracts with an implanted component.

An alternative embodiment incorporates both closed loop control andopen-loop control of therapy in the implanted portion 950 with addedfeatures being supported by an external component, e.g., externalportion 950 or external wearable signal processor 1425. In theembodiment, the external portion 953 operates in conjunction with theimplanted portion 953 to provide an added function. For example, theexternal portion 950 may receive neurological data from the implantedportion 953 through a communications channel (e.g., a hardwire or atelemetry link) and support an added feature in accordance with theneurological data. The added feature may enhance functionality that isprovided by an implanted component and may provide additionalfunctionality to the medical device system. Examples of correspondingadded features may include loop recording of segments of signalsobtained from sensors such as EEG, emergency care features such assounding an alarm or providing a cue to warn the patient of an impendingmedical condition, and making a cell phone telephone call to a caregiver or health care professional if the medical condition of thepatient changes. Additional information may be included with theneurological data and may be indicative of the patient's location, wherethe location may be determined by a Global Positioning System (GPS)receiver that interfaces with the medical device system. Activation of aloop recording function may be established by the physician or by acaregiver or patient with physician guidance using a programmer, e.g.,the programmer 1021. Loop recording causes a segment of one or moremonitored signals occurring around an automatically detected event orselected by the user to be stored in a memory. Programming of implantedportion 953 includes communicating over a communications channel usingprogrammer 1021. The messages sent from programmer 1021 to implantedportion 953 include instructions, parameters, and/or firmware algorithmsthat establish conditions under which the loop recording is activated.Exemplary conditions that may initiate loop recording includingrecognition of specific characteristics of monitored signals such as theoccurrence of a neurological event, established by the physician ingeneral or specifically for the individual and programmed into thememory of the implanted portion 953. Conditions may also include manualtriggers activated by the patient or caregiver or specific times of theday when loop recording should occur. Conditions for loop recording mayalso include certain types of errors or aberrant monitored signals suchas those recognized by signal quality control algorithms that may bestored for later analysis. Manual triggers activated by the patient orcaregiver may include, but are not limited to, subjective or visiblemanifestations of an event, or the point in time when a treatmenttherapy is delivered triggering storage of the signals after anappropriate time delay or when meals or beverages are consumed that mayaffect the physiological parameter being sensed by sensors inimplantable device 953.

An alternative embodiment incorporates the same functionality in bothimplanted portion 953 and external portion 950 but where the externalportion 950 enhances the functionality with added capacity to execute aspecific mode of operation. For example, memory capacity of implantedportion 953 may limit the amount of time and/or number of monitoredsignals stored during loop recording. Periodic use of external portion950 to download the contents of the memory of the implanted portion 953to the external portion 950 for storage until the downloaded data can betransferred to physician programmer 1021 may expand the systemcapabilities. In another embodiment, external portion 950 may include aconnection (e.g., with a modem) to the Internet allowing data about theperformance of the system that is obtained during loop recording to betransmitted to the physician. Alternatively, programming instructionsmay be sent by the physician to the external and/or implanted portions.

Moreover, an external component, e.g., the external portion 950, maysend data to an implanted component, e.g., the implanted portion 953.Programming of implanted device 953 may be accomplished using programmer1021 communicating directly with implanted portion 953. This method isacceptable when all the communication protocols and operational featuresrequired for communication between implanted portion 953 and externalportion 950 are fixed. In those instances when modes of operation mustbe coordinated between implanted portion 953 and external portion 950,it may be more efficient to program the implanted portion 953 by firstprogramming external portion 950, which in turn sends data correspondingto appropriate instructions and receives confirmation of receipt fromimplanted portion 953 via antenna 955. Those skilled in the art willrecognize that the reverse operation of programming implanted portion953 that in turn communicates with external portion 950 is possible.However, this mode of operation requires two telemetry operations whileprogramming of external device 950 directly can be accomplished using ahard wire connection.

In one embodiment of multi-modal operation, the operation by implantedportion 953 of features requiring an external portion 950 may bedependent upon an on-going communication between implanted portion 953and external portion 950. In one instance, implanted device 953periodically sends a signal to external device 950 confirming its modeof operation by providing an indication of the presence of the externalportion 950. (If the implanted portion 953 does not receive a signalfrom the external portion 950 within a specified period of time, theimplanted portion 953 may assume that the external portion 950 is notconnected to the implanted portion 953.) Implanted portion 953 continuesto support features requiring the external portion 950 until such timethat implanted portion 953 detects that external portion 950 is nolonger available. At this point, implantable portion 953 transitions toanother mode of operation that does not require the external portion950. In such a case, the implantable device 953 supports features thatcan be supported without interaction to the external device 950. Forexample, the implantable portion 953 may continue delivering treatmenttherapy in the open-loop mode (i.e., without feedback using neurologicaldata), even though the implantable portion 953 delivers treatmenttherapy in the closed-loop mode (i.e., with feedback using neurologicaldata) when operating in conjunction with the external portion 950.

Multi-Modal operation may also include simultaneous operation of severalmodes of operation of a medical device system, where a plurality offeatures may be supported during the same treatment interval.Simultaneous operation may occur with different medical device systemarchitectures such as external system 100, the hybrid system as shown inFIGS. 9 and 10, or a hybrid system with telemetry booster 1415 (as shownin FIG. 14). In this embodiment, a first feature may support treatmenttherapy in an open loop fashion such as a prophylactic treatment toprevent a medical condition. Simultaneously, a second feature maysupport an algorithm that monitors sensors for prescribed conditionsthat trigger delivery of incremental therapy. In one embodiment, thedevice in FIG. 13 may employ an infusion system to provide apharmaceutical agent in a continuous or intermittent manner to reducethe likelihood of a neurological condition from occurring. However,under prescribed conditions, the medical device system may also providestimulation to respond to an instantaneous change in the medicalcondition in a closed-loop fashion. The incremental therapy may occur inresponse to changes in the values of sensors or a trigger input by thepatient. The triggers or sensor changes may also start loop-recording ofsignals or values of parameters (corresponding to a third feature)and/or communicate with health care professionals via telephone/cellphone links (corresponding to a fourth feature). For example, there maybe communications to the patient with instructions to take oralmedication.

The medical device system may support multi-modal operation, in whichoperation is modified in accordance with the configuration of themedical device system. In one embodiment, the medical device systemoperates as a closed-loop stimulator, in which stimulation therapy isadjusted in accordance with signal measurements and analysis that isperformed. However, if the closed-loop control is removed from theconfiguration, the medical device system operates as an open loopstimulator, in which electrodes with associated implanted electronicsgenerate stimulation therapy.

Scoring of Sensed Neurological Signals

The system may further contain software modules or programs for scoringthe severity of sensed neurological signals relating to a nervous systemdisorder. In particular, the system may monitor sensed neurologicalsignals and compute a relative severity of the events associated withthe neurological signal. Thus, each seizure detection may be rankedbased on the relative severity score. This process may be performed foreach neurological signal that is sensed by the system. Moreover thisprocess may be performed in an implanted device or an external device.If performed in the implanted device, the ranked information may betelemetered to the external device for further processing and/ordisplay.

A seizure event may include, for example, a detected specified event anda reported event. Examples of a detected specified event include,without limitation, an occurrence of a maximal intensity, an extent ofelectrographic spread, or a number of detection clusters per unit timeexceeding a corresponding predetermined threshold. A detected specifiedevent may be associated with a detection cluster, in which the timeduration of the detection cluster or the number of detections within adetection cluster may be further specified. A reported event is aseizure event that the patient perceives and reports, for example, by abutton press.

The system may be programmed to analyze one or more particular featuresof the sensed neurological signal and automatically assign to eachseizure event a “relative severity score” (RS) that is based on theparticular feature(s) being considered. Examples of features to beranked include, without limitation, a maximum ratio (i.e., peakinstantaneous intensity level or ratio of foreground seizure activityvs. background activity), duration of the seizure detection, spread(number of monitoring elements involved in the event as previouslydiscussed in the context of FIG. 22), number of clusters per unit time,number of detections within a cluster, duration of an event cluster,mean or median duration of a detections, and an inter-seizure interval.

By having a relative severity score for each seizure event, the externalsystem 200 may thereby rank events by severity relative to other eventsand provide a means of visually displaying (i.e., loop recordings) andlisting (summary records) the information. To determine severity, aformula using algorithm-based measures of seizure activity may be usedby relating the duration, intensity, and extent of electrographic spreadof a seizure. The external system 200 allows the user to determine whichevents are to be included/excluded from relative severity scorecomputations. This feature may be programmable. The external system 200has a means of manually and/or automatically computing the subject'sRelative Severity Minimum (RS Min), in which the lowest relativeseverity score associated with clinical manifestations or otherbehaviors indicative of seizure activity is used to minimize theprobability of missing clinical seizures. Seizures with scores close toor above the RS Min not associated with event markings or recorded in aclinician's notes may then be visually reviewed to determine if theyhave clinical manifestations.

In an embodiment, the system is capable of computing the relativeseverity of detected specified events within a comparable parameterconfiguration. Each detected specified event has four associatedfeatures to be measured: (1) max ratio; (2) duration; (3) spread; and(4) investigator classification. These features can be denoted by thevector X=(R, D, I; C), where R=max ratio, D=duration, I=Number ofinvolved channels, and C=investigator classification (i.e., TPC, TPNC,FP, or NR). “TP” denotes either TPC or TPNC, and denotes “True Positive”detection. TPC denotes a “True Positive Clinical” detection withclinical manifestations, and TPNC denotes a “True Positive Non-Clinical”detection absent clinical manifestations. FP denotes a “False Positive”and NR denotes a detection that has thus far been “Not Reviewed.” Thefollowing describes a specification for the relative severity functionthat uses all such detection clusters (obtained from analysis sessionsthat have comparable parameter configurations) as inputs and produces asan output a severity score for each such detection cluster.

In some embodiments, it may be difficult to detect clinicalmanifestations. For example, a seizure may be clinically apparent onlyif a patient is performing some action when the seizure occurs. In otherembodiments of the invention, additional investigator classificationsmay be defined. For example, an investigator classification“Non-classifiable” or “Difficult to Classify” may be used if theinvestigator cannot determine is not able to classify the event. Also,an investigator classification “Epileptiform Discharges” may be definedto help identify the impact that treating epileptiform discharges mayhave on seizure frequency.

The relative severity scores are computed using an “interpolatingempirical probability function” (defined below) derived from alldetection clusters in the comparable parameter set that have beenpreviously scored by the investigator as TRUE POSITIVES. (However,scoring is not limited to TRUE POSITIVES an may encompass otherinvestigator classifications.) The function also utilizes the possibleranges of R, D, and I in the calculation (e.g., in the a priori casewhen there are no TPs marked yet because no review has been performed).For example, suppose these ranges are:R_(min)=D_(min)=I_(min)=0,R_(max)=6550, D_(max)=65536 (frames), I_(max)=8.

Suppose that there are N prior true positive detection clusters fromamong M total detection clusters (0≦N≦M). It is assumed that alldetection clusters are associated with the same comparable detectionparameter configuration. The relative severity computations areperformed separately and independently on each different parameterconfiguration's set of detection clusters. The relative severity scoreof a particular detection cluster Y=(R, D, I; C) may be determined bythe following formula:RS(Y)=Round(100*(p ₁ +p ₂ +p ₃)/3)wherep ₁ =P(R; R _(min) , R _(max) , {R _(j)|cluster j is a TP}),p ₂ =P(D; D _(min) , D _(max, {D) _(j)|cluster j is a TP}), andp ₃ =P(I; I _(min) , I _(max) , {I _(j)|cluster j is a TP}).where P is the interpolating empirical probability function (IEPF)described below.

The interpolating empirical probability function can now be described.Given some empirical data values z₁, z₂, . . . , z_(N) which are sortedin increasing order and which lie in a range [z_(min), z_(max)] (withz_(min)<z_(max)), define the interpolating empirical probabilityfunction, P(x; z_(min), z_(max), {z₁, . . . , z_(N)}), as follows. Inthe first case where N>0:

${P\left( {{x;z_{\min}},z_{\max},\left\{ {z_{1},\ldots\mspace{11mu},z_{N}} \right\}} \right)} = \left\{ \begin{matrix}0 & {{\text{if}\mspace{14mu} x} < z_{\min}} \\{\frac{1}{N + 1}\left( \frac{x - z_{\min}}{z_{1} - z_{\min}} \right)} & {{\text{if~~}z_{\min}} \leq x < z_{1}} \\{\frac{1}{N + 1}\left( {i + \frac{x - z_{i}}{z_{i + 1} - z_{i}}} \right)} & {{\text{if~~}z_{i}} < x < {z_{i + 1}\mspace{14mu}\text{for~~some}\mspace{14mu} i}} \\{\frac{1}{2\left( {N + 1} \right)}\left( {1 + {\sum\limits_{j = 1}^{N}\left( {1_{\{{z_{j} \leq x}\}} + 1_{\{{z_{j} < x}\}}} \right)}} \right)} & {{\text{if~~}x} = {z_{i}\mspace{14mu}\text{for~~some}\mspace{14mu} i}} \\{\frac{1}{N + 1}\left( {N + \frac{x - z_{N}}{z_{\max} - z_{N}}} \right)} & {{\text{if~~}z_{N}} < x \leq z_{\max}} \\1 & {{\text{if~~}z_{\max}} < x}\end{matrix} \right.$

Here, 1_({.}) denotes the indicator function of the set {.}. In thesecond case where N=0:

${P\left( {{x;z_{\min}},z_{\max},{\{\}}} \right)} = \frac{{\min\left\{ {{\max\left\{ {x,z_{\min}} \right\}},z_{\max}} \right\}} - z_{\min}}{z_{\max} - z_{\min}}$

FIGS. 28-33 depict various examples. As an example, suppose there are 4scored True Positive detection clusters as follows:

R_(max) D (s) I C 103.1 35.2 6 TPC 115.6 41.3 4 TPNC 34.7 18.9 6 TPNC189.9 55.1 8 TPC

Next suppose 4 more detection clusters are obtained which are presentlynot reviewed as follows:

R_(max) D (s) I C 200.3 12.6 5 NR 49.5 83.2 6 NR 2653.2 4.2 1 NR 122.46.9 3 NR

The following table illustrates the results of determining the RS scorefor each detection.

R_(max) p₁ D p₂ I p₃ RS TP 103.1 0.4 35.2 0.4 6 0.5 43 115.6 0.6 41.30.6 4 0.2 47 34.7 0.2 18.9 0.2 6 0.5 30 189.9 0.8 55.1 0.8 8 0.8 80 NR200.3 0.80032704 12.6 0.133333 5 0.3 41 49.5 0.24327485 83.2 0.801818 60.5 52 2653.2 0.87746105  4.2 0.0444444 1 0.05 32 122.4 0.61830417  6.90.0730159 3 0.15 28

Note that the interpolating empirical probability functions used todetermine the p_(i) values are determined using only the TP values, thenevaluated for both the TP and NR detection clusters to determine theseverity.

If, instead, all of the detection clusters had not yet been scored (orat least not scored as TPs), the severity computations would be changedas shown in the following table.

R_(max) p₁ D p₂ I p₃ RS NR 103.1 0.0157 35.2 0.0112 6 0.7500 26 115.60.0176 41.3 0.0131 4 0.5000 18 34.7 0.0053 18.9 0.0060 6 0.7500 25 189.90.0290 55.1 0.0175 8 1.0000 35 200.3 0.0306 12.6 0.0040 5 0.6250 22 49.50.0076 83.2 0.0264 6 0.7500 26 2653.2 0.4051 4.2 0.0013 1 0.1250 18122.4 0.0187 6.9 0.0022 3 0.3750 13

On the other hand, if all had been scored as TPs, then the RS scoreswould be evaluated as shown in the following table.

R_(max) p₁ D p₂ I p₃ RS TP 103.1 0.3333 35.2 0.5556 6 0.6667 52 115.60.4444 41.3 0.6667 4 0.3333 48 34.7 0.1111 18.9 0.4444 6 0.6667 41 189.90.6667 55.1 0.7778 8 0.8889 78 200.3 0.7778 12.6 0.3333 5 0.4444 52 49.50.2222 83.2 0.8889 6 0.6667 59 2653.2 0.8889 4.2 0.1111 1 0.1111 37122.4 0.5556 6.9 0.2222 3 0.2222 33

The above examples illustrate the determination of the relative severityscore with a data corresponding to a mixture of true positive detectionclusters and not reviewed detection clusters, with data correspondingonly to true positive detection clusters, and to data corresponding onlyto not reviewed detection clusters.

In an embodiment, the medical device system may be modularly expandablein order to add a feature that may enhance existing functionality orthat may support additional functionality. An external component, e.g.,external portion 950, may include a module that supports the addedfeature. The module may be implemented with dedicated hardware and/orcomputer-executable instructions that are performed by an associatedprocessor. In another embodiment, the module may be associated withanother external component that couples to the external component. Ascan be appreciated by one skilled in the art, a computer system with anassociated computer-readable medium containing instructions forcontrolling the computer system can be utilized to implement theexemplary embodiments that are disclosed herein. The computer system mayinclude at least one computer such as a microprocessor, digital signalprocessor, and associated peripheral electronic circuitry.

Thus, embodiments of the SCORING OF SENSED NEUROLOGICAL SIGNALS FOR USEWITH A MEDICAL DEVICE SYSTEM are disclosed. One skilled in the art willappreciate that the present invention can be practiced with embodimentsother than those disclosed. The disclosed embodiments are presented forpurposes of illustration and not limitation, and the present inventionis limited only by the claims that follow.

1. A computer-implemented method for scoring a severity of aneurological event associated with a nervous system disorder, thecomputer-implemented method comprising: (a) determining using aprocessor that one or more sensed neurological signals represent aplurality of neurological events; (b) identifying using a processor atleast one feature of each of the plurality of neurological events,wherein the plurality of neurological events are selected from the groupconsisting of a detection cluster event and a reported event; (c)computing using a processor a relative severity score for each of theplurality of neurological events using the at least one feature; and (d)ranking using a processor the plurality of neurological events byseverity using the relative severity scores.
 2. The method of claim 1,wherein the at least one feature identified in (b) is selected from thegroup consisting of a duration of a seizure detection, a spread, anumber of clusters per unit time, a number of detections within acluster, a duration of an event cluster, a duration of a detection, andan inter-seizure interval.
 3. The method of claim 1, further comprising:(e) communicating the ranked plurality of neurological events to anexternal device.
 4. The method of claim 1, further comprising: (e)displaying the ranked plurality of neurological events.
 5. The method ofclaim 1, wherein the ranking in (d) is performed by an implanted device.6. The method of claim 1, wherein the identifying the at least onefeature in (b) comprises: (i) using algorithm-based measures of activityof the nervous system disorder.
 7. The method of claim 1, wherein eachof the plurality of neurological events is a seizure and the computingthe score in (c) comprises: (i) relating duration, intensity, and extentof electrographic spread of the neurological event.
 8. The method ofclaim 1, wherein the feature is selected from the group consisting of anumber of monitoring elements involved in the neurological event, anumber of clusters per unit time, a number of detections within adetection cluster, a duration of a detection cluster, a duration of adetection, and an inter-seizure interval.
 9. The method of claim 1,wherein the computing the score in (c) comprises: (i) computing arelative severity minimum, wherein the lowest relative severity scoreassociated with clinical manifestations or other behaviors indicative ofa nervous system disorder activity is useful for minimizing aprobability of missing clinical events.
 10. The method of claim 1,wherein the one or more sensed neurological signals are received from amonitoring element and are selected from the group consisting of achemical signal, a biological signal, a temperature signal, a pressuresignal, a respiration signal, a heart rate signal, a ph-level signal,and a peripheral nerve signal.
 11. The method of claim 1, wherein thenervous system disorder is selected from the group consisting of aperipheral nervous system disorder, a mental health disorder, and apsychiatric disorder.
 12. A computer-implemented method for determiningthe severity of a detection cluster comprising: (a) determining using aprocessor that one or more sensed neurological signals represent aplurality of detection clusters; (b) identifying using a processor atleast one feature of each of the detection clusters; (c) computing usinga processor a relative severity score for each of the detection clustersusing the identified at least one feature; and (d) ranking using aprocessor the plurality of detection clusters by severity using therelative severity scores.
 13. The method of claim 12, wherein the atleast one feature identified in (b) is selected from the groupconsisting of a spread of the detection cluster, a number of detectionclusters per unit time, a number of detections within the detectioncluster, a detection cluster severity, and an inter-seizure interval.14. The method of claim 12, wherein the computing of the relativeseverity score in (c) comprises: (i) computing a relative severityminimum, in which the lowest relative severity score associated withclinical manifestations or other behaviors indicative of a nervoussystem disorder activity is useful for minimizing a probability ofmissing clinical events.
 15. The method of claim 12, wherein thecomputing of the relative severity score in (c) comprises: (i) allowinga user to exclude a certain event from being scored.
 16. The method ofclaim 12, wherein (b)-(d) occur after a respective detection cluster hasended.
 17. A computer-implemented method for determining the severity ofa detected neurological event comprising: (a) receiving one or moreneurological signals; (b) processing using a processor the one or moreneurological signals to detect a plurality of neurological events; (c)characterizing using a processor at least one feature of each of theplurality of detected neurological events; and (d) computing using aprocessor a relative severity score for each of the neurological eventsbased on the at least one feature.
 18. The method of claim 17, furthercomprising: (e) ranking the plurality of neurological events based onthe relative severity scores for each of the neurological events. 19.The method of claim 18, wherein the at least one feature characterizedin (c) is selected from the group consisting of a spread of a detectioncluster, a number of detection clusters per unit time, a number ofdetections within a detection cluster, a detection cluster severity, andan inter-seizure interval.
 20. The method of claim 18, wherein thecomputing in (d) comprises: (i) computing a relative severity minimum,in which the lowest relative severity score associated with clinicalmanifestations or other behaviors indicative of a nervous systemdisorder activity is useful for minimizing a probability of missingclinical events.
 21. The method of claim 18, wherein the computing in(d) comprises: (i) allowing a user to exclude a certain neurologicalevent from being scored.
 22. The method of claim 17, wherein (c)-(d)occur after a respective detected neurological event has concluded.